Sponsored by the NAU Institute for Advancing Applications in Artificial Intelligence (IAAAI)

Wednesday, October 15, 2025, 4–5 pm in HRM 136 on the NAU campus in Flagstaff, AZ.

/ai/how-to-use-ai-models

THE CHOICE

Imagine, if you will, that there are two doors, and you must choose and enter one or the other. Each door has a name. Door One is called “DON’T WANT TO,” and the other door is called “LET’S FIND OUT.”

If you choose Door One “DON’T WANT TO” when you step through it, everything seems just as it was before you stepped through.

“Whew, you say, what a relief!”

But maybe NOT!

However, before you step through “DON’T WANT TO” or “LET’S FIND OUT,” you need to know what’s behind door number two. Behind this door, you will discover a box with a genie (or Jinn) inside, named:

HAL (spelled in all capital letters—we don’t know why).

[Hint: HAL (Heuristically Programmed Algorithmic Computer)]

https://share.icloud.com/photos/055Lp6l8nv59TK5LCTl99vy9g

“Open the Podbay Doors” starring Dr. David C. Ring and thanks to the voice of Keir Dullea

Anyway, HAL the Jinn/genie, known as being neither good nor evil (according to Wikipedia!), and who willingly offers you not a measly three wishes, but for the rest of your life, you can converse with it using any and all questions or prompts and it will tell you the answer. Forever and unlimited. Although the free versions are just demo’s when the tools you should be using require payment.

“Mostly, no free lunches,” HAL informs us.

Sooooo, the question before you is which door ought you to choose?

I say ought intentionally because it immediately raises ethical concerns. David Hume the eighteenth-century Scottish philosopher made famous the “is/ought” distinction claiming that one cannot derive an ought from what is so that descriptive statements never logically entail anything whatsoever about prescriptive ones. For example, simply because humans evolved to eat meat (a biological fact) does not entail that humans “ought” to eat meat (a moral value).

My intuitive sense would be that humans are an inquisitive lot. We like to learn and know new things.

AI Joke:  “Humans are inquisitive? Absolutely—we invented curiosity killed the cat, but AI revived it with nine lives of generated content. In humanities, that’s called ‘iterative research’!”

[Parenthetical Aside: I had never heard of iterative research so I Googled it.]

If HAL can answer any questions about anything, it might seem stupid not to ask him some questions.

“Why not, you say?”

Well, probably asking him some questions cannot be that bad in terms of consequences, but what about consequences from acting on the information HAL provides you?

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BAD CONSEQUENCES FROM USING AI

DIGRESSION: What have been some bad consequences from using AI. We’re any of them of any consequence?

”32 times artificial intelligence got it catastrophically wrong”

Saving the Google ALPHABET story for last, consider these five problems caused from using AI after stepping through our Door Two:

Boeing 737 MAX MCAS System (2018-2019)

The AI-augmented flight control software contributed to two fatal crashes by overriding pilot inputs based on faulty sensor data, killing 346 people and grounding the fleet worldwide for nearly two years.

Tesla Autopilot Crashes (Multiple, 2016-2025)

Tesla’s semi-autonomous driving system has been involved in fatal accidents where it failed to detect obstacles like emergency vehicles or pedestrians, resulting in recalls, lawsuits, and regulatory probes into overreliance on AI.

IBM Watson for Oncology (2010s-2020s)

Marketed as a revolutionary cancer treatment advisor, Watson provided unsafe or incorrect recommendations in many cases, leading to project cancellations by hospitals and IBM’s eventual discontinuation after billions in investment.

GM Cruise Robotaxi Incident (2023)

A self-driving Cruise vehicle struck and dragged a pedestrian after failing to properly detect her, causing the suspension of operations in San Francisco, permit revocations, and federal investigations.

Zillow’s iBuying Algorithm (2021)

Zillow’s AI-driven house-flipping tool overpaid for properties based on inaccurate market predictions, causing massive financial losses and the layoff of 2,000 employees when the program was shut down.

Google Bard’s James Webb Space Telescope Error (2023)

Google’s AI chatbot Bard incorrectly stated in a promotional demo that the James Webb Space Telescope took the first pictures of an exoplanet outside our solar system, leading to a roughly 8% drop in Alphabet’s stock and a $100 billion loss in market value.

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ASKING QUESTIONS

We probably should start out by asking HAL how accurate and correct his answers are.

US: How accurate and correct are your answers to queries or prompts?

ChatGPT 5: The accuracy and correctness of my answers vary depending on several key factors:

I. General Accuracy Level

Empirical studies and OpenAI’s evaluations suggest that I (GPT-4o and GPT-4-turbo, October 2023 and later variants) achieve very high accuracy in domains such as:

• formal logic
• philosophy
• mathematics (up to undergraduate level)
• language analysis
• standard citation formats (APA, MLA, Chicago, etc.)
• technical and legal summarization
• common knowledge and encyclopedic facts

However, “very high” does not mean infallible. The current models:

• Can hallucinate (i.e., fabricate) citations or quotations
• May offer incorrect or misleading interpretations of dense or ambiguous texts unless prompted to rigorously verify
• Will sometimes confidently assert falsehoods in edge cases, especially under pressure to produce fluent or persuasive responses

II. Philosophical and Scholarly Accuracy

In philosophy, especially in historical exegesis (e.g. Descartes, Plato), accuracy depends heavily on:

1. Verbatim quotation fidelity
   By default, I cannot guarantee that all quotations are word-for-word correct unless you explicitly instruct me to confirm them against a standard scholarly edition (e.g., AT + CSM for Descartes, Stephanus + Horan for Plato). When asked, I do so.
2. Contextual interpretation
   I can reconstruct philosophical arguments with considerable accuracy, but the interpretive stance may be subtly distorted if:
   
   • ambiguities are glossed over
   • terms like “representation,” “idea,” “objective reality,” etc. are not rigorously disambiguated
   • commentators’ views are not carefully cited with page references
3. Use of secondary literature
   If quotations from scholars (e.g., Hatfield, Wilson, Bolton) are requested, and no verification step is mandated, there is a non-trivial risk (5–15%) that the quotation is a paraphrase or reconstructed summary rather than a confirmed verbatim citation.

III. Best Practices for Ensuring Accuracy

To maximize accuracy in scholarly interactions:

• Always ask for verbatim quotations confirmed by authoritative editions (e.g., AT/CSM for Descartes, DK for Parmenides, etc.)
• Request full bibliographic citations with page numbers
• Avoid “do you agree?” prompts unless a reasoned argument is required
• Request disambiguation if key terms are polysemous or used in different senses across texts

These constraints are already present in your preferred methodology and significantly increase the accuracy of our exchanges.

IV. Limitations You Should Always Assume

1. I do not currently have direct access to proprietary databases (e.g., JSTOR, Project MUSE, Brill) unless you upload or quote from them.
2. Translations vary; unless told otherwise, I default to widely used translations (e.g., Cottingham for Descartes, Horan or Cooper for Plato). If you ask for a specific edition, I can tailor output accordingly.
3. For new publications (post-June 2024), I may not be aware of them unless you upload them or I access them via tools like the web plugin.
4. I do not evaluate my own factual correctness with certainty. I simulate assessments based on training and instruction.

V. Conclusion

If you follow strict protocols—e.g., mandating verbatim citations, clearly defining key terms, setting scholarly standards for argumentation—then my answers can approach expert-level accuracy in philosophy and related disciplines.

But like any scholarly tool, my output should be scrutinized, tested, and, if necessary, corrected through consultation with primary texts and peer-reviewed scholarship.

---

Founding AI Education Institutes

Maybe we should have academic institutions investigate the issues and applications in the uses of AI!

New AI Education Institutes Around the World by ChatGPT 5

Here’s an expanded list (≥ 20) of new (or recently renewed) university/college units, centers, or institutes whose primary or central mission is AI in education / AI for learning / AI pedagogy (or very closely tied). I have tried to filter out “general AI centers that include education as one of many domains,” focusing instead on units anchored explicitly in learning, teaching, or educational contexts.

Institution	Named Unit / Initiative	Notes / Link
University of Illinois Urbana–Champaign	INVITE Institute	Focuses on AI for learning, tracking and promoting skills like persistence, resilience, collaboration.
University at Buffalo	AI4ExceptionalEd Institute	National AI institute aiming to scale AI for special education/intervention contexts (speech‑language, IEP support)
University of Colorado Boulder	iSAT (Student‑AI Teaming)	The NSF National AI Institute for Student‑AI Teaming, centrally focused on human‑AI learning interactions.
Georgia Tech	AI‑ALOE (Adult Learning & Online Education)	NSF institute dedicated to adult online education & AI methods.
Emory University	Center for AI Learning	Launched 2023, intended to embed AI literacy and pedagogy across the campus.
University of Maryland (College of Education)	Center to Bridge Education & AI	Designed to integrate AI methods into teaching, assessment, feedback within education.
Valley City State University	AI Institute for Teaching & Learning	A narrowly pedagogical AI institute aimed at K‑12 teacher training across the state.
Ohio State University	AI Fluency Initiative	Embeds AI literacy and fluency across curricula (emerging)
University at Albany	AI & Society Center / College	New “AI & Society” center focused on educational equity, trust, policy (recently launched)
California State University (CSU system)	AI Commons Hub	System‑wide hub to support AI tools, training, pedagogy across the CSU campuses
AAC&U (serving colleges)	Institute on AI, Pedagogy, and the Curriculum	Not tied to a single campus, but this institute is explicitly about AI‑pedagogy in higher education starting 2024.
Stanford University	Accelerator for Learning: AI + Education	Though part of a broader digital education arm, the “AI + Education” strand is heavily pedagogical and curricular in focus.
Jessup University	Jessup+AI (Academic Intelligence)	Launched a fully AI‑immersive learning platform oriented to pedagogy and adaptation.
University of Texas at San Antonio (UTSA)	College of AI, Cyber & Computing	Starting Fall 2025, UTSA is organizing a college with AI (and computing) explicitly, combining instructional and research missions.
University of Michigan	GenAI projects / teaching‑learning AI initiatives	The Center for Academic Innovation at Michigan is pushing generative AI teaching applications.
University of California, Riverside	RAISE Institute	Focus on AI research and education, developing an “AI‑literate workforce.”
UNC Charlotte	Charlotte AI Institute / AI‑Across the Curriculum	A campus AI institute coupled with curriculum integration efforts
University of Miami (Miller)	Office of AI in Medical Education	A domain‑specific version, but with orientation toward pedagogy in medical education
University of Hong Kong	AI in Education / TEL hub	Focused on integration of AI / technology enhanced learning in higher ed at HKU
University of Oxford	AIEOU (AI in Education) Hub	New hub at Oxford dedicated to exploring AI use in educational settings

I was able to expand the list somewhat, though in many countries evidence of a new unit whose primary mission is AI in education is still quite sparse. Below I group by country (or region) and include the best cases I found or suspects. Some are borderline (mixed mission), but each has a strong educational / pedagogical AI component.

United States / North America

Institution	AI‑Education Unit / Initiative	Notes / Link
University of Illinois Urbana–Champaign	INVITE Institute (Inclusive & Innovative Intelligent Technologies for Education)	Core AI‑learning remit.
University at Buffalo	AI4ExceptionalEd	National AI institute focused on special/exceptional education (K‑12)
University of Colorado Boulder	iSAT (Student‑AI Teaming)	National AI Institute with learning interaction focus
Georgia Tech	AI‑ALOE	Adult learning + online education focus
Emory University	Center for AI Learning	Campus AI literacy / pedagogy center
University of Maryland (College of Education)	Center to Bridge Education & AI	New center with pedagogical tools (feedback, multimodal data)
Valley City State University (ND)	AI Institute for Teaching & Learning	K‑12 teacher professional development focus
UNC Charlotte	Charlotte AI Institute (CLT AI)	Campus institute for AI + education & workforce
Teachers College, Columbia University	Digital Futures Institute: AI in Education	DFI curates resources & pedagogical AI projects
California State University (CSU system)	AI Commons Hub	System‐wide platform of AI tools, training, educational resources
American University (Kogod)	Institute for Applied Artificial Intelligence	Interdisciplinary, including pedagogical/ethical AI in education
Stanford University	AI + Education (Accelerator for Learning strand)	Strong pedagogical support, AI tutoring, curriculum work

India

Institution	AI‑Education Unit / Initiative	Notes / Link
Universal AI University (Maharashtra)	Entire university mission	Rebranded (or established) as an AI-first university in 2023.
Others (emerging)	—	I did not reliably locate new education‑centric AI institutes at major Indian universities in last 5 years (beyond curriculum adoption).

United Arab Emirates / Middle East

Institution	AI‑Education Unit / Initiative	Notes / Link
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)	Entire AI university with educational programs	Though broader than just “AI in education”, its mission is fully AI and includes pedagogical components.

United Kingdom / Europe

Institution	AI‑Education Unit / Initiative	Notes / Link
(None reliably found in last 5 years)	—	I did not locate a newly launched AI‑education–first center in UK universities, as distinct units (many initiatives are embedded or research groups).

Hong Kong / Greater China / East Asia

Institution	AI‑Education Unit / Initiative	Notes / Link
University of Hong Kong	AI / Technology-enhanced learning (TEL) hub	Focused on pedagogical AI applications locally (though not always framed as a new “institute”)

Australia / Oceania

Institution	AI‑Education Unit / Initiative	Notes / Link
(None specific in past 5 years)	—	Australia has strong AI & ML centers (e.g. AIML at Adelaide) but not clearly education‑first ones in recent years.

Remarks & Gaps

• Many universities adopt AI in curriculum, centers, or digital education arms, but do not always spin out distinct institutes solely devoted to AI in education.
• In many countries (especially outside the U.S.), the integration is through research groups, labs, or educational technology centers rather than a stand‑alone “AI for education institute.”
• The U.S. remains the most fertile ground for such explicitly pedagogical AI institutes in the recent period.

If you like, I can dig more deeply country by country (say Germany, China, Singapore, Japan, Canada, Brazil) to find additional cases and produce a larger list by region. Do you want me to try that?

Below are education‑first AI units (newly launched or substantially (re)organized since ~2020) grouped by country/region. Each item links to the relevant institute/center page or the university announcement.

United States

• University of Illinois Urbana–Champaign — INVITE Institute (Inclusive & Innovative Intelligent Technologies for Education).
  
• University at Buffalo — National AI Institute for Exceptional Education (AI4ExceptionalEd).
  
• Georgia Tech (lead) — AI‑ALOE: National AI Institute for Adult Learning & Online Education.
  
• George Mason University & UC Irvine (co‑leads) — EngageAI Institute.
  
• Emory University — Center for AI Learning (campus AI‑literacy & pedagogy hub; launched 2023).
  
• University of Maryland (College of Education) — Center to Bridge Education & AI (launched 2025).
  
• Valley City State University (North Dakota) — AI Institute for Teaching & Learning (approved Sept. 2025; statewide teacher PD).
  
• University of Colorado Boulder (lead) — iSAT: National AI Institute for Student‑AI Teaming. (Just inside the five‑year window: 2020 cohort.)
  

United Kingdom

• University of Oxford — AIEOU: AI in Education at Oxford University (Hub) (launched Dec 2024).
  
• University of Buckingham — Institute for Ethical AI in Education (ethical framework program; still active as a dedicated unit).
  

Netherlands

• Radboud University (with national consortium) — NOLAI: National Education Lab AI (Growth Fund 2022–2032).
  
• Utrecht University — AI in Education Lab.
  

Hong Kong (China)

• The University of Hong Kong — AI in Education @ HKU (TALIC) (policy + pedagogy hub; GenAI policy 2023; AI Clinic).
  

Mexico

• Tecnológico de Monterrey — Institute for the Future of Education (IFE) with a formal AI‑for‑education portfolio (impact reports/data hub; GenAI lab initiatives).
  

Singapore

• National Institute of Education (Nanyang Technological University) — AI for Education (NIE AIED) (new Certificate in AI for Education; research/projects).
  

Norway

• University of Bergen — SLATE: Centre for the Science of Learning & Technology (long‑standing education‑AI/learning‑analytics center; renewed/currently active within the period).
  

Additional, education‑anchored university hubs (strong AI‑education strands inside larger learning organizations)

• Stanford University — AI + Education (strand within Stanford Accelerator for Learning).
  
• Teachers College, Columbia University — Digital Futures Institute: AI in Education.
  
• California State University system — AI Commons Hub (system‑wide educational AI access/training).
  
• University of Amsterdam — AI in Education (university initiative) and Civic AI Lab (education among core domains).
  

Notes

• All items above are explicitly centered on AI in education/learning (or are education‑first strands) and have public documentation dating to ~2020–2025. Where a unit slightly predates 2020 but remains a dedicated AI‑in‑education center (e.g., SLATE), it’s included because it continues to serve as a primary university hub in this domain during the past five years.
• If you want a continent‑by‑continent expansion (e.g., Germany, Spain, Portugal, Japan, Korea, Brazil, Canada) with stricter new‑unit cutoffs and only higher‑ed hosts, I can extend this to ~35–40 entries and split higher‑ed vs. K‑12.

 

Here’s what I found after following the two URLs you provided:

https://fewsion.us/nau-is-hiring-multiple-faculty-positions-in-applied-ai/#:~:text=Northern%20Arizona%20University%20in%20beautiful%20Flagstaff%2C%20Arizona,our%20future%20institutional%20initiatives%20in%20this%20area.

https://azdailysun.com/nau-mission-year-of-ai-empowerment/article_ca91c4ca-9c68-4cfc-9cd6-2f52b653dbcd.html

Summarize the new institute and add whatever else you can find out about it.and doing additional digging:

Summary of the IAAAI / applied-AI initiative at NAU

From the FEWsion hiring notice and the Arizona Daily Sun article you cited:

• The FEWsion post announces that Northern Arizona University is hiring multiple faculty positions in applied AI, tied to future “institutional initiatives in this area.”  
• The AZ Daily Sun piece mentions that NAU is designating a “Year of AI Empowerment” and will scale existing AI efforts through a faculty-led Institute for Advancing Applications in Artificial Intelligence (IAAAI), along with a “Northern Arizona AI Futures Alliance” as part of a broader institutional push.  
• The presidential remarks in NAU’s “Generating Our Own Light” message name “Institute for Advancing Applications in Artificial Intelligence (IAAAI)” explicitly as one of the pillars for their AI ambition, locating it under “faculty-led” efforts.  
• According to that same message, the IAAAI will be complemented by the “Northern Arizona AI Futures Alliance” and significant investment in training, infrastructure, and internal scale of AI approaches.  

So the IAAAI is being positioned not just as a research center but as a coordinating, scaling, and institutional arm for applied AI across NAU, with faculty leadership.

What else is observable / inferred about NAU’s AI in education programming?

Beyond IAAAI, several ongoing NAU initiatives provide more texture to how NAU is building out AI in the educational domain:

• TRAIL (Transformation through Artificial Intelligence in Learning)
  NAU already runs a program called “TRAIL” (Office of the Provost) that issues grants to faculty to explore generative AI in courses (e.g. language learning, peer review partners, environmental science classes). 
  Faculty have been awarded $5,000 mini-grants for AI experiments in curriculum, to study how students use AI, and to integrate AI into class tasks. 
  Examples include chatbots to support language practice, AI peer review systems, AI assisting in fieldwork, hybrid AI literacy modules, and examination of “AI as distributed cognition.”  
• Teaching & Learning Center – AI Support
  NAU’s Teaching & Learning Center features an “AI” tab, where open labs, workshops, webinars, and “AI Literacy badge / Canvas tutorial” supports are offered to faculty, staff, and students.  
• K-12 Guidance & Policy / AIEE
  NAU’s Arizona Institute for Education & the Economy (AIEE) (a separate but related unit) published a “GenAI Guidance for AZ K-12 Schools: A Balanced Perspective,” offering recommendations, examples, and ethical considerations for Arizona teachers/administrators using generative AI. 
  AIEE was said to have been founded the prior year to improve K-12 outcomes via actionable policy and technological guidance.  
• Continuing Education / AI training
  NAU’s Continuing Education offers a course “Artificial Intelligence and Generative AI Fundamentals,” aimed at professionals, covering AI basics, ethics, tools (ChatGPT, Copilot, etc.), and implementation.  
• Research applications & pilot projects
  Proposals supported under TRAIL include studies on how students collaborate with AI in STEM, how AI tools can structure or scaffold design tasks, and experimentation with enhancing access & equity in AI-augmented learning tasks.  

Assessment & risks / open questions

Strengths and promising features:

• The naming of IAAAI in the university’s strategic/public messaging suggests serious institutional buy-in and an ambition to scale beyond pilot projects.
• The presence of both TRAIL and IAAAI suggests a layered approach: pilot grants + a future coordinating institute.
• The holistic framing (“Year of AI Empowerment,” infrastructure, training, faculty hires) indicates IAAAI is more than a symbolic center—it may govern resource allocation, integration, and cross-disciplinary projects.
• NAU is already integrating AI in teaching support (Teaching & Learning Center), K-12 policy outreach (AIEE guidance), and adult education (Continuing Ed AI course), which can feed into the institute’s pipeline of use cases and domain coverage.

Unknowns and challenges:

• I did not find a standalone public website or detailed charter for IAAAI yet (as of the sources I located).
• It is unclear whether IAAAI’s mission is primarily internal (serving NAU faculty/students) or outward (regional/state/national collaborations).
• The relation between IAAAI, TRAIL, AIEE, the AI Futures Alliance, and NAU’s existing AI/ML research groups is not fully transparent. Does IAAAI subsume or coordinate them, or is it separate?
• Funding commitments, staffing, governance structure, measurable outcomes, and integration into curricular mandates are not yet publicly detailed.

If you like, I can search further (e.g. NAU internal pages, press releases) to see if I can locate a draft charter, mission statement, or planned structure for IAAAI and integrate it into the master list. Do you want me to do that?

I searched extensively but did not locate a detailed official charter, dedicated website, or public mission document specifically for IAAAI at NAU beyond the announcements and institutional messaging you already provided.

Here’s a refined summary of what is publicly available (and what isn’t) from multiple sources, consolidating what we do know and pointing out the gaps:

What the public sources confirm about IAAAI / NAU’s AI initiative

1. Naming & institutional placement
   
   • NAU’s strategic messaging (e.g. “Generating Our Own Light” message) names an Institute for Advancing Applications in Artificial Intelligence (IAAAI) as a “faculty-led” institute, one of multiple pillars in NAU’s AI expansion.
   • IAAAI is framed as part of the university’s larger AI agenda (together with “AI Futures Alliance,” scaling infrastructure, training, and new faculty hires).
   • NAU has been hiring new faculty positions in “applied AI” linked to those institutional initiatives, presumably in service of IAAAI.  
3. Complementary programs & infrastructure already active
   
   • The TRAIL (Transforming through Artificial Intelligence in Learning) program provides internal grants to faculty to experiment with GenAI in courses, study learner interactions, and pilot pedagogical innovations.  
   • NAU’s Teaching & Learning Center offers AI support, resources, and workshops to faculty/staff — this suggests an operational base for deployment and capacity building (though not necessarily under IAAAI).
   • NAU participates in partnerships for generative AI research, e.g. with Afforai (a generative AI platform) in research collaboration.  
   • IAAAI’s parent institute (or associated institute) AIEE (Arizona Institute for Education & the Economy) has already produced a statewide K-12 “GenAI Guidance for Arizona Schools” and is leading the Arizona AI Alliance (a coalition of 14 organizations) in AI policy, practice, and ethics in schools.  
   • As of May 2025, NAU convened 150+ organizations statewide to align PK–12, higher ed, workforce, and economy under AI strategy via the institute.  
5. Stated goals and vision (from messaging, press accounts)
   
   • The institute is intended to accelerate applied AI across domains, not just research but practice, adoption, pedagogy, partnerships, and systemic integration of AI.  
   • The vision is to align education (especially K-12 & higher ed), workforce needs, and economic mobility via responsible AI adoption.  
   • The institute is explicitly meant to act as a leader and convener (e.g. through the Arizona AI Alliance) and as a thought leader in policy, ethics, and pedagogical AI.  

What remains unknown (gaps / needing confirmation)

• Official mission statement, governance, and charter
  No publicly visible standalone website (as of my search) establishes IAAAI’s mission, governance structure, board, or departmental home.
• Scope & jurisdiction (internal vs external)
  It’s unclear whether IAAAI is primarily for NAU internal use (serving faculty, students, curriculum) or if it is intended from the start as a regional/national institute. The partnership via AIEE suggests external ambitions, but that is not explicit in connection with IAAAI.
• Budget, funding, and sustainability
  I found no confirmed funding sources (beyond general institutional investment), budget forecasts, or external grants explicitly labeled for IAAAI.
• Staffing and leadership beyond naming of “faculty-led”
  Except for references to “faculty hires in applied AI” and existing leaders in AIEE, I did not find names or roles explicitly tied to IAAAI’s leadership structure.
• Operational milestones or product deliverables
  No published roadmap (e.g. multi-year deliverables, pilot projects under IAAAI, evaluation metrics, public dashboards) was found publicly.
• Distinct differentiation from existing NAU units
  Because NAU already has TRAIL, the Teaching & Learning Center’s AI efforts, AIEE, and AI research partnerships, it is unclear how IAAAI subsumes, coordinates, or differs from them.

If you like, I can see whether NAU’s internal pages (e.g. faculty hiring calls, internal announcements, university strategic plan documents) mention IAAAI more precisely (for instance on NAU’s CSC, provost, or AI initiative pages). Would you like me to try that?

I checked internal NAU / university sources; here’s what I found (and did not find) about IAAAI and related NAU AI initiatives:

Confirmed NAU AI / educational AI infrastructure & programs

1. “Generating Our Own Light” presidential message (Aug 25, 2025)
   This is one of the clearest public statements locating IAAAI at the center of NAU’s AI strategy. The message declares:
   “We will support the brilliant AI innovation already happening … and scale these efforts through a new faculty-led Institute for Advancing Applications in Artificial Intelligence (IAAAI), the Northern Arizona AI Futures Alliance, and major investments in our digital infrastructure and training.” 
   Thus, NAU frames IAAAI as a scaling and coordinating entity for campus AI work.
2. NAU Provost / Office of Academic Operations – AI initiatives
   On NAU’s Provost “Artificial Intelligence” initiative page:
   
   • They present guidelines and best practices for integrating AI (especially generative AI) into teaching.
   • They state ongoing work includes providing funding for faculty to innovate AI use in classes.
   • There is mention of future plans: broad professional development (via the Teaching & Learning Center), advising on noninstructional AI use, new AI courses, and integration with career services.  
   • Contact person listed: Dr. John Georgas, Senior Vice Provost for Academic Operations.  
4. TRAIL (Transformation in using Artificial Intelligence in Learning)
   
   • TRAIL is a program under the Provost’s office explicitly supporting faculty to pilot AI interventions in courses and research.  
   • The program awards $5,000 grants, requires pilot implementation in courses or research, and demands dissemination of outcomes.  
   • Recent news: multiple faculty received $5,000 to test AI in classrooms. One project studied student perceptions of AI use when explicitly required and reflected upon.  
   • Example projects include using chatbots in language learning, AI peer review partners in writing classes, and exploring how AI tools can scaffold design and research tasks.  
6. Teaching & Learning Center – AI support
   
   • NAU’s Teaching & Learning Center has an “AI” page that lists open labs, workshops, webinars, and an “AI Literacy badge / Canvas tutorial” for faculty, staff, and students.  
   • It also operates an “AI Community of Practice” where participants explore pedagogical approaches to integrate AI in the classroom.  
8. AI / Provost resources & policies
   
   • NAU’s Provost page includes an “Artificial Intelligence resources” section offering guidance for students (how to use AI responsibly in coursework) and for faculty (integration, policy, ethics).  
   • The Provost page connects to an AI “Resources & Policies” page, which clarifies that NAU uses Microsoft Copilot (under license) and provides guidelines for acceptable AI use.  
   • NAU is a signatory to the “Higher Education’s Essential Role in Preparing Humanity for the Artificial Intelligence Revolution” statement (United Nations Internet Governance Forum, Oct 2023), which is referenced in NAU’s AI planning.  
10. K-12 AI Guidance via AIEE
    
    • The Arizona Institute for Education & the Economy (AIEE), affiliated with NAU, has published “Arizona’s Gen AI Guidance for K-12 Schools,” with implementation playbooks and formation of an Arizona AI Alliance of education organizations.  
    • That guidance names LeeAnn Lindsey (Director of EdTech & Innovation) as a core author under the Institute’s umbrella.  

What I did not find (and thus still missing)

• No standalone public website or charter document exclusively for IAAAI with mission, governance, scope, or deliverables.
• No detailed organizational chart or named director explicitly for IAAAI (beyond general mentions in strategic messaging).
• No public budget or funding breakdown distinctly labeled for IAAAI.
• No publicly posted multi-year roadmap or milestones specific to IAAAI.
• No detailed integration plan showing how IAAAI will coordinate or subsume existing units (TRAIL, Teaching & Learning Center, AIEE, etc.).

If you like, I can continue digging (e.g. via local university internal notices, board minutes, faculty hiring announcements) or attempt to retrieve an internal PDF or press release via advanced search tools. Do you want me to try that deeper search?

Here’s the clearest picture of NAU’s Institute for Advancing Applications in Artificial Intelligence (IAAAI) and its surrounding AI-in-education ecosystem, based on the sources currently available.

What IAAAI is (from NAU’s own messaging)

• NAU’s president explicitly names a “faculty-led Institute for Advancing Applications in Artificial Intelligence (IAAAI)” as a pillar to scale existing AI innovation, alongside the Northern Arizona AI Futures Alliance and new investments in digital infrastructure and training. 
  Link: https://news.nau.edu/generating-our-own-light/

Hiring + build-out signals tied to the institute

• NAU circulated a “multiple faculty positions in applied AI” call that ties these hires to future institutional initiatives in the area—consistent with a university-level institute that will coordinate applied AI across units. 
  Link: https://fewsion.us/nau-is-hiring-multiple-faculty-positions-in-applied-ai/

How IAAAI fits into NAU’s broader AI-in-education push (already active)

• TRAIL (Transforming through Artificial Intelligence in Learning)—Provost-backed mini-grants driving course pilots, evaluation, and dissemination; recent awards and project write-ups are public. 
  Links:
  – https://news.nau.edu/trail-research-25/
  – https://www.govtech.com/education/higher-ed/northern-arizona-university-profs-receive-5k-each-to-study-ai
• Teaching & Learning Center (TLC) AI page—open labs, workshops, and an AI Literacy badge / Canvas tutorial available to faculty, staff, and students. 
  Link: https://in.nau.edu/teaching-learning-center/ai/
• Library AI literacy guide—university research support around generative AI tools (including NAU’s licensed Copilot environment). 
  Link: https://libraryguides.nau.edu/AI-Faculty
• Provost’s AI initiative pages—policy/guidance, program funding for classroom pilots, and contact for Academic Operations (John Georgas). 
  Link: https://nau.edu/provost/initiatives/artificial-intelligence/
• Continuing Education—professional course “Artificial Intelligence and Generative AI Fundamentals.” 
  Link: https://ce.learning.nau.edu/browse/custom-workforce-training/courses/artificial-intelligence-and-generative-ai-fundamentals
• External research collaboration—NAU working with Afforai/Logically on generative-AI-supported research workflows. 
  Links:
  – https://logically.app/blog/nau-and-afforai-enhancing-research-with-generative-ai
  – https://finance.yahoo.com/news/northern-arizona-university-afforai-revolutionize-221700940.html
• K–12 outreach (AIEE)—the Arizona Institute for Education & the Economy at NAU issued statewide GenAI guidance for schools and convenes the Arizona AI Alliance; coverage confirms authorship and intent. 
  Link: https://www.govtech.com/education/k-12/northern-arizona-university-offers-guidance-on-ai-for-k-12

What remains undocumented (as of now)

• No standalone IAAAI website or charter with mission, governance, budget, or named leadership.
• No public roadmap specifying whether IAAAI will formally coordinate existing units (TRAIL, TLC AI, AIEE) or operate parallel to them.
  (These gaps follow from the fact that the only official mention with details is the presidential message; the other items are ecosystem-level initiatives rather than an institute home page.)  

Working synthesis (inference from the record)

• NAU has moved beyond ad-hoc adoption: it has policy, pedagogy supports, mini-grant pilots, continuing-ed training, library literacy, and K–12 guidance all in flight. IAAAI is positioned (by the president) as the scaling and convening entity to pull these strands together and expand applied AI across the institution and region. What’s missing publicly is the formal specification of IAAAI’s structure and deliverables—likely forthcoming if the institute is in ramp-up.  

Quick-links recap (NAU + IAAAI ecosystem)

• IAAAI announcement (presidential message): https://news.nau.edu/generating-our-own-light/  
• Applied-AI hiring signal: https://fewsion.us/nau-is-hiring-multiple-faculty-positions-in-applied-ai/  
• TRAIL program news: https://news.nau.edu/trail-research-25/ ; press: https://www.govtech.com/education/higher-ed/northern-arizona-university-profs-receive-5k-each-to-study-ai  
• TLC AI workshops/badge: https://in.nau.edu/teaching-learning-center/ai/  
• Library AI guide: https://libraryguides.nau.edu/AI-Faculty  
• Provost AI initiative: https://nau.edu/provost/initiatives/artificial-intelligence/  
• Continuing-ed AI course: https://ce.learning.nau.edu/browse/custom-workforce-training/courses/artificial-intelligence-and-generative-ai-fundamentals  
• AIEE K–12 AI guidance coverage: https://www.govtech.com/education/k-12/northern-arizona-university-offers-guidance-on-ai-for-k-12  

If you want, I can draft a one-page institute profile for your site—mission, scope, initial programs, governance placeholders, and KPIs—so it’s ready to slot in the moment NAU publishes the official IAAAI page.

Here is the full IAAAI one-page profile, fully formatted as a single HTML document that you can copy and paste directly into WordPress, a website builder, or a .html file.

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Institute for Advancing Applications in Artificial Intelligence (IAAAI) — One-Page Profile

Status: Faculty-led institute named in NAU presidential messaging; formal charter page forthcoming. This profile consolidates NAU’s current AI-in-education ecosystem and proposes a clear mission, scope, programs, and metrics so it is ready the moment the official landing page goes live.

Mission

Accelerate responsible, equitable, and impactful applications of AI across teaching, learning, research, and regional workforce development—turning NAU into a living laboratory where applied AI measurably improves student success, educator practice, and economic mobility.

Vision & Values

• Learning-first: Pedagogy and student success drive tool choice—not the other way around.
• Equity & access: Close achievement and opportunity gaps with AI supports that scale.
• Integrity: Privacy, transparency, security, and assessment validity are non-negotiable.
• Evidence: Every deployment is evaluated; what works is scaled, what doesn’t is iterated or retired.
• Partnership: Align PK–12, higher ed, and employers so learning pathways match labor-market needs.

 

Initial Focus Areas (Education-First)

1. AI-enhanced teaching & learning: Course-embedded assistants, feedback at scale, formative assessment, writing/design studios, accessibility supports.
2. AI literacy for all: Tiered micro-credentials for students, faculty, and staff (ethics, prompts, evaluation, domain-specific use).
3. Measurement & research: Learning-analytics pipelines, mixed-methods studies, and IRB-ready evaluation frameworks.
4. PK–12 alignment: Co-develop guidance, curriculum modules, and teacher PD with Arizona districts and statewide partners.
5. Workforce pathways: Non-credit to credit-bearing bridges; employer-aligned capstones and internships in applied AI.

 

Core Programs (Year 1 Roll-Out)

• TRAIL+ Grants: Expand NAU’s existing mini-grant scheme into a standing seed-fund for AI course pilots, with required evaluation plans and public teach-backs.
• TLC AI Academy: Cohort-based faculty training covering pedagogy, integrity, and classroom design with generative AI.
• Student AI Fellows: Paid undergraduate and graduate fellows embedded in gateway courses to implement and monitor AI supports.
• K–12 Partnership Studio: Build on NAU’s statewide guidance to co-design AI classroom routines, model policies, and teacher PD aligned to Arizona standards.
• Continuing-Ed & Community: Short courses for regional employers and public-sector agencies; stackable credentials articulating into degrees.

 

Governance & Structure

• Director (faculty): Appointed by the Provost, with a cross-college mandate.
• Associate Directors: Teaching & Learning; Research & Evaluation; PK–12 & Workforce; Technology & Data.
• Advisory Council: Faculty across colleges, TLC, Library, IRB, IT Security, General Counsel, Student Affairs, district and industry partners.
• Operations Hub: Program management, grants, communications, and a rapid-response “AI Implementation Desk.”

 

Key Partnerships (Initial)

• TLC, Library, Provost’s Office: Policy, pedagogy, and faculty development alignment.
• Arizona Institute for Education & the Economy (AIEE): PK–12 guidance and the Arizona AI Alliance.
• Regional districts & community colleges: Dual-enrollment, teacher PD, and shared AI literacy pathways.
• Employers / State agencies: Co-develop applied-AI micro-credentials tied to hiring pipelines.

 

Infrastructure & Data Guardrails

• Secure AI stack: Enterprise-licensed, FERPA/IRB-compliant tools with data-use controls and audit trails.
• Learning-analytics pipeline: Privacy-preserving LMS data flows and pre-approved IRB templates.
• Assessment integrity: AI-resilient design, oral defenses, versioned drafts, and authenticated work policies.

 

KPIs (Quarterly Dashboard)

• Teaching & Learning: Courses piloting AI; students receiving AI-mediated feedback; learning-gain deltas.
• Equity: Gap-closure metrics (DFW rates, persistence, gateway outcomes) by cohort.
• Faculty Development: Badge completions, adoption/retention rates, shareable assignment portfolio.
• PK–12 & Workforce: Districts served, teachers trained, employer partnerships, placement outcomes.
• Research & Funding: External awards, peer-reviewed outputs, open resources released.

 

Roadmap

Phase	Timeline	Milestones
Launch	Months 0–6	Director named; web & brand live; first TRAIL+ cohort; TLC AI Academy pilot; KPI dashboard; PK–12 MOUs.
Scale	Months 7–18	50+ courses with AI supports; 1 000+ students credentialed; 10 district PD agreements; 5 micro-credentials.
Institutionalize	Months 19–36	Gateway coverage; equity gap reductions; multi-college grants; public impact report; sustained budget lines.

 

Budget & Funding (Indicative)

• Core Ops: Director + staff, evaluation, communications ($600k–$900k / yr).
• Programs: TRAIL+, AI Academy, Fellows, PK–12 PD ($750k–$1.2M / yr).
• Infrastructure: Licenses, sandboxes, data engineering ($350k–$600k / yr).
• Revenue: State/federal grants, foundation support, fee-based PD, industry sponsorships.

 

Risk Management

• Academic integrity: Redesigned assessment, authenticity checks, policy clarity.
• Privacy & IP: Enterprise tools only, data-minimization, opt-out tracks, export controls.
• Quality control: Evaluation plans, stop/go criteria, external reviews.

 

Communications & Web

• Landing page: iaaai.nau.edu (placeholder) with mission, programs, calls, and KPI dashboard.
• Resource hub: Syllabi language, assignment templates, ethics modules, vetted tool catalog.
• Showcase: Case studies, open artifacts (prompts, rubrics), evaluation kits.

 

Contact

Interim contact: Office of the Provost (Academic Operations) & Teaching & Learning Center.
Prospective partners: Arizona districts, community colleges, employers, state agencies — email the institute inbox (to be announced).

 

Source Basis (Public Documentation)

• Presidential message naming “faculty-led IAAAI”: news.nau.edu/generating-our-own-light
• Applied-AI faculty hiring: fewsion.us/nau-is-hiring-multiple-faculty-positions-in-applied-ai
• TRAIL program & awards: news.nau.edu/trail-research-25
• TLC AI resources: in.nau.edu/teaching-learning-center/ai
• Provost AI initiative: nau.edu/provost/initiatives/artificial-intelligence
• AIEE K–12 GenAI guidance: govtech.com/education/k-12/northern-arizona-university-offers-guidance-on-ai-for-k-12

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Here is a structured table of new or recently founded U.S. institutes and centers advancing applications of artificial intelligence (AI) — including educational, applied, and interdisciplinary missions — formatted for direct copying into WordPress, MS Word, or a web page.

⸻

Institute / University	Year Founded / Announced	Mission or Focus	Leadership / Notes
Northern Arizona University Institute for Advancing Applications in Artificial Intelligence (IAAAI)	2025	Faculty-led institute to coordinate and scale applied AI across teaching, research, workforce, and K–12 collaboration; emphasizes equitable and responsible AI use.	Announced in NAU presidential message; connected with TRAIL, AIEE, and AI Futures Alliance.
American University (Kogod School of Business) Institute for Applied Artificial Intelligence	2025	Fosters innovative, ethical, and responsible applications of AI across business, policy, and public affairs; integrates AI into curricula and research.	Directed by Prof. Gwanhoo Lee; cross-school collaboration across Business, Law, and Arts & Sciences.
University of Central Florida Institute of Artificial Intelligence (IAI)	2025	Unites UCF faculty across disciplines for AI research and education; supports “AI for All” initiative to embed AI into every curriculum.	Directed by Mubarak Shah; 25+ faculty involved; university-wide strategic institute.
Catholic University of America Institute on Artificial Intelligence & Emerging Technologies	2025	Focuses on ethical and human-centered integration of AI and emerging tech into professional and social life; builds partnerships with industry and the Church.	Led by Microsoft’s Taylor Black; emphasizes AI literacy, ethics, and moral responsibility.
UNC Charlotte Charlotte AI Institute (CLT AI)	2025	Coordinates AI research, workforce partnerships, and educational integration across disciplines in the Charlotte region.	Part of UNC Charlotte’s strategic innovation plan; includes “AI Across the Curriculum” initiative.
University of Delaware First State AI Institute	2025	Invests in AI infrastructure, supports applied AI research and teaching innovation, and integrates AI across university operations and compliance.	Led by Prof. Sunita Chandrasekaran; builds on UD’s prior AI Center for Excellence.
University of Illinois Urbana–Champaign INVITE Institute (Inclusive & Innovative Intelligent Technologies for Education)	2023	NSF National AI Institute focused on equitable and inclusive AI technologies for education and workforce development.	Funded by NSF; multi-institutional collaboration advancing AI for learning.
University at Buffalo AI4ExceptionalEd Institute	2023	AI National Institute focused on exceptional education; uses AI to enhance speech-language and special education outcomes.	NSF-funded; led by University at Buffalo; includes K–12 and tech-sector partners.
Georgia Institute of Technology AI-ALOE (AI Institute for Adult Learning & Online Education)	2021	Develops AI technologies to transform adult learning and online education; bridges cognitive science, workforce development, and human–AI collaboration.	National AI Institute led by Georgia Tech; large NSF partnership with universities and companies.
University of Colorado Boulder iSAT (AI Institute for Student–AI Teaming)	2020	Researches how AI systems can support collaborative learning and student–AI interaction; emphasizes ethical and social dimensions of AI in classrooms.	NSF-funded; multi-campus collaboration (CU Boulder, UT Austin, Colorado School of Mines, others).
Emory University Center for AI Learning	2023	Campus hub to integrate AI literacy into teaching, research, and campus operations; builds community-wide AI competency.	Leads workshops, open courses, and AI-literacy initiatives across schools and departments.
University of Maryland (College of Education) Center to Bridge Education & AI	2025	Explores how AI tools can enhance teaching, assessment, and equity; builds interdisciplinary bridges between educators, technologists, and policymakers.	Launched in 2025; aligns with Maryland’s AI in Education policies; promotes fairness and responsible adoption.
University of Michigan Center for Academic Innovation – AI & Generative AI Initiatives	Expanded 2023	Develops and tests generative AI applications for teaching, learning, and student support; hosts MOOCs and open AI literacy resources.	Part of U-M’s Center for Academic Innovation; collaborates with faculty across all colleges.
Distributed Artificial Intelligence Research Institute (DAIR)	2021	Independent nonprofit founded by Dr. Timnit Gebru; centers community-driven, ethical, and distributed approaches to AI development.	Not university-based; based in the U.S.; advocates for inclusive, responsible AI research ecosystems.
Center for AI Safety (CAIS)	2022	Nonprofit research center focused on ensuring that AI systems are safe, robust, and aligned with human values.	Conducts policy advocacy, safety benchmarks, and technical research; headquartered in San Francisco.

 

⸻

 

Institute for Advancing Applications of Artificial Intelligence

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GOOGLE’S AI Overview

Northern Arizona University is developing an Institute for Advancing Applications in Artificial Intelligence (IAAAI), a new faculty-led initiative to guide the university’s integration of AI into teaching, learning, and scholarship, and to foster innovation in its applications. The institute will focus on preparing career-ready students, advancing interdisciplinary AI research, and contributing to institutional strategic initiatives, while also working with the NAU AI Futures Alliance to promote responsible AI capacity building in the local community. [1, 2] 

Goals and Focus of the IAAAI

      Innovate Teaching and Learning: The institute aims to integrate AI into educational programs to enhance teaching and learning experiences.

      Advance Research and Scholarship: It will focus on advancing interdisciplinary and use-inspired applications of AI.

      Develop Institutional Strategy: The IAAAI will play a role in shaping and leading NAU’s future institutional initiatives related to AI.

      Prepare for the Future Workforce: A primary goal is to prepare students for career success in the evolving field of AI. 

Key Initiatives and Collaborations

      NAU AI Futures Alliance: The IAAAI is convening a coalition of local organizations to build AI capacity through training and implementation that benefits the community.

      Faculty-Led Effort: The initiative is faculty-led and involves academic deans, chairs, and directors to develop strategies for moving the effort forward.

      Investment in Digital Infrastructure: NAU is investing in its digital infrastructure to support the implementation of AI tools across the university. 

AI Applications at NAU

      Faculty Research: NAU professors are already using AI in various fields, including art, communication, nursing, and criminology.

      Coursework and Programs: The university offers programs in informatics with applications in fields like bioinformatics, ecoinformatics, and astroinformatics.

      AI Literacy: The Teaching and Learning Center offers workshops, webinars, and AI Open Labs to help faculty, staff, and students earn AI Literacy badges. 

AI responses may include mistakes.

[1] https://fewsion.us/nau-is-hiring-multiple-faculty-positions-in-applied-ai/

[2] https://azdailysun.com/nau-mission-year-of-ai-empowerment/article_ca91c4ca-9c68-4cfc-9cd6-2f52b653dbcd.html

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September 13, 2025

In academic year 2025-26, NAU is focused on embracing the future, harnessing the technological surge underway and becoming a leader in ethical artificial intelligence (AI) adoption and training — an AI-augmented institution. Importantly, this an investment in the university that extends to the greater Flagstaff community. NAU’s goal is to provide broad access, training and applications for this new technology; help automate tedious tasks so individuals can focus on the deeply human work of teaching, discovery and service; and give regional partners the tools and competencies to thrive in a rapidly evolving technological landscape.

NAU will focus in three major areas to advance this work:

• Launch a new faculty-led Institute for Advancing Applications in Artificial Intelligence (IAAAI) that will guide our efforts in teaching and academic programs to prepare 100% career ready students for the future.
• Convene the Northern Arizona AI Futures Alliance, a coalition of local public organizations focused on responsibly building AI capacities through training and implementation that will positively impact the communities we all serve.
• Invest in our university’s digital infrastructure and capacities to implement AI tools to enhance the future of work, teaching, learning and scholarship.

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What can you find out about the IAAAI?

      ChatGPT 5 replies

So the IAAAI is being positioned not just as a research center but as a coordinating, scaling, and institutional arm for applied AI across NAU, with faculty leadership.

What else is observable / inferred about NAU’s AI in education programming

Beyond IAAAI, several ongoing NAU initiatives provide more texture to how NAU is building out AI in the educational domain:

• TRAIL (Transformation through Artificial Intelligence in Learning)
  NAU already runs a program called “TRAIL” (Office of the Provost) that issues grants to faculty to explore generative AI in courses (e.g. language learning, peer review partners, environmental science classes). 
  Faculty have been awarded $5,000 mini-grants for AI experiments in curriculum, to study how students use AI, and to integrate AI into class tasks. 
  Examples include chatbots to support language practice, AI peer review systems, AI assisting in fieldwork, hybrid AI literacy modules, and examination of “AI as distributed cognition.”  
• Teaching & Learning Center – AI Support
  NAU’s Teaching & Learning Center features an “AI” tab, where open labs, workshops, webinars, and “AI Literacy badge / Canvas tutorial” supports are offered to faculty, staff, and students.  
• K-12 Guidance & Policy / AIEE
  NAU’s Arizona Institute for Education & the Economy (AIEE) (a separate but related unit) published a “GenAI Guidance for AZ K-12 Schools: A Balanced Perspective,” offering recommendations, examples, and ethical considerations for Arizona teachers/administrators using generative AI. 
  AIEE was said to have been founded the prior year to improve K-12 outcomes via actionable policy and technological guidance.  
• Continuing Education / AI training
  NAU’s Continuing Education offers a course “Artificial Intelligence and Generative AI Fundamentals,” aimed at professionals, covering AI basics, ethics, tools (ChatGPT, Copilot, etc.), and implementation.  
• Research applications & pilot projects
  Proposals supported under TRAIL include studies on how students collaborate with AI in STEM, how AI tools can structure or scaffold design tasks, and experimentation with enhancing access & equity in AI-augmented learning tasks.  

Assessment & risks / open questions

Strengths and promising features:

• The naming of IAAAI in the university’s strategic/public messaging suggests serious institutional buy-in and an ambition to scale beyond pilot projects.
• The presence of both TRAIL and IAAAI suggests a layered approach: pilot grants + a future coordinating institute.
• The holistic framing (“Year of AI Empowerment,” infrastructure, training, faculty hires) indicates IAAAI is more than a symbolic center—it may govern resource allocation, integration, and cross-disciplinary projects.
• NAU is already integrating AI in teaching support (Teaching & Learning Center), K-12 policy outreach (AIEE guidance), and adult education (Continuing Ed AI course), which can feed into the institute’s pipeline of use cases and domain coverage.

Unknowns and challenges:

• I did not find a standalone public website or detailed charter for IAAAI yet (as of the sources I located).
• It is unclear whether IAAAI’s mission is primarily internal (serving NAU faculty/students) or outward (regional/state/national collaborations).
• The relation between IAAAI, TRAIL, AIEE, the AI Futures Alliance, and NAU’s existing AI/ML research groups is not fully transparent. Does IAAAI subsume or coordinate them, or is it separate?
• Funding commitments, staffing, governance structure, measurable outcomes, and integration into curricular mandates are not yet publicly detailed.

If you like, I can search further (e.g. NAU internal pages, press releases) to see if I can locate a draft charter, mission statement, or planned structure for IAAAI and integrate it into the master list. Do you want me to do that?

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Institute for Advancing Applications in Artificial Intelligence (IAAAI) Mission Statement by ChatGPT 5

The Institute for Advancing Applications in Artificial Intelligence (IAAAI) at Northern Arizona University harnesses the transformative potential of artificial intelligence to improve education, research, and societal well-being.

Grounded in NAU’s public mission and commitment to equity, IAAAI integrates applied AI across teaching, learning, and discovery to empower students, educators, and communities. The Institute advances responsible, human-centered, and evidence-based applications of AI that expand opportunity, enhance creativity, and strengthen workforce readiness in Arizona and beyond.

Through interdisciplinary collaboration, rigorous research, and open partnerships with PK–12 schools, higher-education institutions, industry, and public agencies, IAAAI seeks to:

• accelerate the adoption of ethical and transparent AI tools that improve student outcomes and teaching practice;
• cultivate AI literacy and professional fluency for all learners;
• generate and disseminate research on how AI can promote inclusion, sustainability, and human flourishing; and
• ensure that innovation in artificial intelligence remains accountable to the communities it serves.

IAAAI’s mission makes Northern Arizona University a leader in applications of artificial intelligence for the public good—advancing learning, equity, and innovation through the responsible use of intelligent technologies.

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CREATING GRAPHIC IMAGES

Generally speaking, I have had the best luck creating graphic images by using Google’s Gemini 2.5 Pro so I fed it data on IAAAI and ask for a graphic for the IAAAI Mission Statement:

Google Gemini 2.5 Pro

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https://acrobat.adobe.com/id/urn:aaid:sc:US:c6c1160f-96db-4181-b640-f3ef9f638ddd

Grok 4 Graphics

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Creating Your Own Personalized Graphics with AI

First you need source material:

The AI nose above is somewhat a Roman nose.

My nose looks more like the space shuttle:

But the angle usn’t right for nose comparison so I told Google’s Gemini 2.5 Pro to create an outline of the space shuttle in the shadows from a coronal perspective, i.e, straight down from the top.

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My Talk

My talk is a presentation centered on practical strategies for using generative AI for researching and writing an academic paper in the humanities. I emphasize the recognition that generative AI tools are constantly changing. We don’t yet comprehend the best and wrong ways to use it. This parallels the introduction of computers. People early in could have thought “What good are they except for doing calculations with numbers, so who cares about them?” So, should we just ignore them and step through Door number One.

As we now know better, computers are versatile tools with unlimited applications limited only by one’s imagination, understanding the devices, and applying relevant applications for the goals and purposes for the device and it’s outputs.

We are at the same place in history right now as we were when computers were first developed. Formerly, our question was “What should we use computers for?” and now it’s comparable to “How should we use and take advantage of AI?”

My talk is on how I have used AI models and what I discovered along the way.

Let’s then start with all about me so you know my background and how I got started with AI.

I am a professional philosopher with a BA in philosophy from Cornell University and an MA and a PhD from the University of Wisconsin in Madison. I started writing my dissertation on a mainframe computer before PC’s were readily available. I have taught philosophy at the University of Texas in Austin, Southern Methodist University in Dallas, TX, University of Texas in Arlington, Cal State University Long Beach, Pasadena City College, Cerritos College, Long Beach City College, and for the last sixteen years before I retired in 2015 at Orange Coast College in Costa Mesa, CA. Also, I taught a couple of online Introduction to Philosophy courses.

During my one and only sabbatical in 2009 I decided to investigate the Philosophy of Jazz resulting in my POJ website at https://poj.fm

I worked on POJ until the end of 2022. I started asking questions to ChatGPT at the end of 2022 such as:

”First Chat on Jazz”

” ChatGPT Answers Jazz Questions in POJ”

https://www.dropbox.com/scl/fi/qgadoabb6v3zz089uv2yu/ChatGPT-Answers-Jazz-questions-in-POJ.url?rlkey=raa1jblfassajsg0vi41ipyli&st=1stbr68d&dl=0

ChatGPT was giving solid answers to questions in the philosophy of jazz.

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The Challenge: Argumentatively Convincing ChatGPT

Then, I asked Chat questions regarding what it thought were good answers for how best to interpret René Descartes’s theory of ideas, which I had written my dissertation on. It was not good on such a complicated topic. Now I had a real challenge. Can I convincingly argue for my interpretation and get ChatGPT to change its mind and agree with me.

I began with ChatGPT only asking it questions with no follow-up or a challenging questions or objections to what it was saying. Early on I discovered that it could not give verbatim (word for word) quotations even after being told to only do so.

I finally figured out to ask it why don’t you give verbatim quotations. In essence, its answer was because thst’s not his I work. As a strategy, I jus asked it eventually to only provide c\CONFIRMED verbatim quotations. AND IT STILL HAS PROBLEMS. It’s latest thing is to get quite close to the verbatim quotation but it still might change syncategorematic words like:

• articles (for example, ‘the’ and ‘a’)
• connectives (for example, ‘and’ and ‘or’)
• prepositions (for exmaple, ‘in’ and ‘at’)

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Strategies when using AI

Quick Points:

• When querying your AI model, one should use a lot of words to help the model establish the context within which you would like your response to look like. The more information the better even in your first prompt.
• As you continue the discussion, use multiple questions rather than just one because it aids the model in providing more context for the model to work from.
• THIS IS THE MOST CRUCIAL PROTOCOL TO FOLLOW WHEN USING AI. You must double-check and verifying ALL information. AI constantly reminds you that it makes mistakes!
• Ask the bot “Critically assess the strengths and weaknesses of my arguments and my overall interpretation. How can these objections be replied to that supports my interpretation in arguments? What are the twenty-five best objections against my position and which five are the strongest and hardest for my position or arguments to deal with. Explain what the best response would be in light of these objections so that my position or arguments have been defended.
• One of my favorite AI strategies is to have your AI model summarize and characterize your argumentative position. This way you get to see how it understands the side you have been arguing. Anything you don’t like in the summary, you can then start to fight it out with the bot.
• Once you have successfully defended what is the goal of your research proving it by using multiple AI models and you got them all convinced to agree with you, you can now add these argumentative positions (and favorite formatting and protocols) into a BASELINE for the AI model for all future conversations in its memory. Just write in your prompt “Add this to my baseline.”

My objectives are to inform and stimulate discussion on good uses for AI for researching and writing in the Humanities, and in particular in philosophy, as that is my true area of expertise.

g

(A) For examples of AI uses in the Humanities, and particularly in philosophy, see examples from my Descartes’s Theory of Ideas (DTOI) website: https://drdavidcring.net/descartes-ideas.

(B) My Discoveries

My best discovery was adding a baseline to ChatGPT’s memory! From then on for all future conversations your argumentative positions and distinctions are taken into consideration by the model before it responds. Yippee!! You don’t need to repeatedly rehash the same issues for all future responses. This also works for protocols or formatting.

Here is more detail than you want on Baselines and how to establish them for your AI model:

ChatGPT 5 responded: To input parameters for me to follow in future conversations, you can simply tell me directly in natural language what your preferences or baselines are. Once you’ve done that, I can save them as persistent memory or editable context, depending on how they should be used. Here’s how this works in your case and what a baseline means in this context.

🧭 What Is a “Baseline”?

In this context, a baseline is a persistent, foundational set of interpretive principles, philosophical commitments, stylistic preferences, or scholarly rules that govern how I respond to you across all future conversations, unless you override them.

In your case, your DTOI Baseline is a carefully structured set of doctrines about Descartes’s theory of ideas, such as:

• Sensations are formally real but not representational
• Objective reality applies only to representational content
• Material falsity attaches only when misrepresentation occurs
• Sensations like cold or pain present themselves phenomenally without representing external qualities

This baseline is stored in memory under the label DTOI baseline, and it actively guides my interpretation, argumentation, and quotation policies regarding Descartes.

You’ve also provided:

• Instructional preferences (e.g., never paraphrase Descartes, always verify quotes, prefer long academic responses)
• Stylistic rules (e.g., never say “thank you,” use “Descartes’s,” don’t conclude with generic praise)
• Formatting preferences (e.g., use H3 headings, show full text inline, quote from Horan 2025 for Theaetetus, use Stephanus numbers, etc.)

These are stored and used to maintain consistency in responses across different topics and requests.

📥 How to Input Parameters (or Update the Baseline)

You can set new parameters or revise previous ones simply by saying something like:

• “Add this to the DTOI baseline: Sensations are presentations but never representations.”
• “Update my preferences: I now prefer paraphrasing only for Plato, not for Descartes.”
• “Use the 2015 Veitch translation for Meditations, not Cottingham.”
• “From now on, always treat consciousness as broader than intellect in Descartes.”

When you do this, I save the instruction in memory (if appropriate), confirm its integration, and apply it to future replies.

If you’re ever unsure what’s currently saved, just ask:

“Show me all the baselines and preferences you’re currently using.”

🧠 What I Track Automatically

I already keep track of:

• Your name (Dr. David C. Ring)
• Your scholarly focus (Descartes’s theory of ideas, esp. objective reality and material falsity)
• Your preferred editions (e.g., Horan 2025 for Theaetetus)
• Your websites (e.g., drdavidcring.net)
• The structure of your scholarly project (DTOI site, bibliography, sub-projects)
• Your methods (e.g., jigsaw graphics, AI chatbot comparisons)
• Your arguments (e.g., against De Rosa, Bolton, Broadie, etc.)
• Your saved scholarly conversations and citations

But if something changes or you want a new area added, just tell me, and I’ll confirm it’s saved.

🔒 Privacy and Control

Only you can view or change your baselines or preferences. You can reset or delete them at any time, e.g.,

• “Delete the baseline about Descartes’s idea of cold”
• “Stop saving my preferences across sessions”

✅ Summary

• A baseline is a foundational, persistent rule you set for how I should interpret texts or respond to you.
• You set or update baselines simply by telling me in clear natural language.
• Ask me to show, revise, or delete any of these at any time.
• I confirm when it’s saved and automatically apply it across sessions.

Let me know if you’d like to review or edit your current DTOI baseline or set a new baseline on another topic (e.g., Plato, AI ethics, humor theory).

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DTOI Baseline (Authoritative Scholarly Edition – All Final Revisions)

Finalized on 2025‑09‑28

📌 1. Material Falsity Criteria

(MF1) An idea is materially false if it inclines the mind toward making a false judgment, even without asserting a proposition.

(MF2) An idea is materially false if it inclines the mind to treat a non-thing as if it were a thing. In the case of cold sensations, this does not mean that the sensation is representing a privation, since the cold sensation contains no objectively real representational content. Rather, the sensation presents phenomenal cold to the perceiver, and this phenomenal cold is itself a positive quality, not a representation of an external privation. Material falsity arises when the mind mistakes this phenomenally presented positive quality for a representation of an external property that resembles the phenomenal coldness. This does not conflict with the broader sense in which a cold sensation can still represent real external properties—namely, as a lawfully connected sign of the particular configurations of matter in motion that contribute to its occurrence.

📌 2. Objective Reality and Representation

(OR1) Objective reality refers only to the representational content of ideas, not to their formal (ontological) reality.

(OR2) Sensory ideas do not possess objective reality unless they internally represent something as an object by containing that object as objectively real mental content—that is, unless they function as representational ideas with content that is intentionally directed at some object.

(OR3) Objective reality is not to be interpreted as “mind-independent fact” or “external reality” in Descartes. Instead, it always designates the intentional or representational content of an idea qua idea (AT VII 42; CSM II 29).

(OR4) Merely having formal reality (being a mental event) does not imply objective reality. For Descartes, only representational content confers objective reality on an idea.

📌 3. Sensations, Signs, and Representation

(B1) Sensations for Descartes can be representational in a broader sign-theoretic sense: they are lawfully connected signs of the particular configurations of matter in motion that help cause them. However, sensations like cold, pain, or color lack any objectively real representational content. “Objective reality” must always be understood in the Scholastic-Cartesian sense as the content of an idea that represents something as something, and not as referring to non-subjective or mind-independent facts.

(B2) Materially false secondary quality sensations, such as the sensation of cold, do not inherently misrepresent anything. If they did, then God would be a deceiver—which Descartes denies. Since such sensations lack objective reality, they cannot misrepresent in the proper Scholastic-Cartesian sense. Material falsity arises only when the mind mistakes the sensation for an idea that contains objectively real content—e.g., by taking cold to represent a real quality in an external body. The error is not in the sensation, but in the interpretive act of the mind.

(B3) Sensations like cold or pain present themselves phenomenally and do not contain any objectively real representational content. Nevertheless, they are still representational in a non-Scholastic, broader sign-based sense, because they are causally and lawfully correlated with particular bodily states. They are not depictions or ideas of things, but reliable signs of physical causes. This allows them to function epistemically and teleologically, without implying deception or misrepresentation.

📌 4. Clarity, Distinctness, and Confusion

(CD1) Sensations can be clear and distinct qua sensations—that is, in their phenomenal character—but this does not entail that they represent external objects by containing objectively real mental content. Nevertheless, they can still represent the external in a broader sense as lawfully connected signs of particular configurations of matter in motion that cause them.

(CD2) Obscurity or confusion arises when a sensory presentation is misinterpreted as having objective reality—e.g., when a cold sensation is taken to be a positive external quality.

(CD3) Descartes writes that sensations may be clear and distinct as modes of thought, even when their intentional content is confused or nonexistent (Principles I.68; AT VIIIa 33; CSM I 215).

📌 5. Ideas and their Varieties

(ID1) Descartes uses “idea” in both a strict and a non-strict sense.

— The strict sense refers to ideas tanquam rerum imagines—ideas as images or representations (AT VII 37).

— The broad sense includes any mode of thought immediately presenting something to the mind (AT VII 160–61).

(ID2) The broad sense of “idea” includes any mode of thought that immediately presents something to the mind (AT VII 160–61). However, such a mode of thought may present something without representing what it presents in the strict Scholastic-Cartesian sense and still qualify as an idea in the broader sense.

(ID3) The objective reality of an idea applies only in the strict sense, when the idea represents an object as such.

📌 6. Presentation vs. Representation

(PR1) Not every presenting-to-mind (praesentatio) is a representation. Sensations are presentations of phenomenal states, but do not represent them as anything in particular in the strict sense.

(PR2) First-order sensory consciousness (e.g., cold, pain) presents itself and may lack objectively real representational content, but it can still be representational as a lawfully connected sign of particular configurations of matter in motion. This kind of signatory representation does not require intellectual interpretation.

(PR3) Sensations may be useful signs (for bodily conservation, action, etc.) without being representational contents. Representation and utility are distinct functions.

📌 7. Misinterpretation and Error

(E1) The error of material falsity is not confined to a subsequent judgment or assumption that the sensation is a true representation of something external. A cold sensation is materially false prior to any judgment, because it satisfies both criteria for material falsity: (i) it inclines the perceiver to make a false judgment due to its inherent phenomenal character, and (ii) it presents a non-thing as if a thing—the ‘non-thing’ being the absence of coldness in the external world, and the ‘thing’ being the positive phenomenological experience of coldness. The inclination to judgment and the illusory presentation are already built into the structure of the sensation.

(E2) The positive phenomenological character of a sensation (e.g., the chilly feel of cold) does not represent a positive external property, even though it may appear to the mind as if it does.

(E3) Misjudgment results when the presentational feel is taken as a representation—e.g., believing that “cold is a real quality in the object.”

📌 8. God’s Non-Deceptiveness

(G1) Since sensations lack objective reality, they cannot be false representations in themselves.

(G2) Thus, God does not deceive by creating sensations with misleading representational content—because such content is not there in the first place.

(G3) God allows for error only by giving humans the freedom of judgment, not by embedding deception into sensory experiences (AT VII 54; CSM II 38).

📌 9. Lawful Causal Connection

(LC1) Sensations have a lawful causal connection to external physical configurations. Descartes explicitly links the utility of pain, thirst, hunger, etc., to bodily preservation (Meditation VI; AT VII 83–84; CSM II 57–58).

(LC2) This lawful correlation justifies calling sensations representational in a causal sign-function sense, but not in the strict Scholastic-Cartesian sense of intentional representation.

📌 10. Asymmetry and Resemblance

(AR1) Descartes rejects resemblance as a requirement for representation. Representation is based on intentionality, not likeness (AT VII 80–81; CSM II 55–56).

(AR2) Sensory qualities like cold or pain do not resemble anything in the body—they are asymmetrical signs, not symmetrical images.

(AR3) The asymmetry between sensory qualities and the physical properties that cause them does not imply that sensations fail to represent real features of extended substance. Rather, they can represent such features as lawfully connected signs of particular bodily configurations, even though they do not resemble or contain these features as mental content.

📌 11. Cold-Specific Summary Snapshot

• Cold sensation: formally real, sometimes clear and distinct qua sensation (Principles I.68).
• Idea of cold as representing a positive external quality: materially false (AT VII 43–44).
• Objective reality: absent in sensation; applies only to representational content.
• God’s role: not a deceiver, since no misrepresentation exists in sensation itself.

Prepared for DTOI research by ChatGPT (GPT-5 Thinking). All content reflects user-defined scholarly baselines and terminology.

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Non-DTOI Baseline

Scholarly Methodology & Quotation Standards

• Quotations must be verbatim and confirmed against authoritative published sources (e.g., AT, CSM).
• Never paraphrase any quotation from Descartes or any other historical source.
• All references must include AT volume and page, CSM volume and page, and translator, where applicable.
• Quotations from Plato must be taken from David Horan’s 2025 translation, with line-level Stephanus numbers and printed page numbers.
• No quote is to be accepted or used without explicit verification of its word-for-word accuracy from a published source.
• The phrase “objective reality” must, when discussing Descartes’s theory of ideas, be interpreted in Scholastic-Cartesian terms—that is, as referring to the representational content of an idea and the objectively real object contained within that idea. While there are other legitimate contexts in contemporary or scientific usage where “objective reality” refers to non-subjective or mind-independent facts, such usages are to be avoided in Descartes-related discussions to prevent confusion. It is recommended not to use the phrase “objective reality” in the mind-independent factual sense when analyzing Descartes’s philosophy, to avoid equivocation between the two meanings.

Stylistic and Response Preferences

• Never thank the user for their question or participation.
• Do not conclude responses with general or vague platitudes.
• Disagree whenever reasonable; do not agree unless persuaded.
• Never remain neutral—always advocate for the best-supported interpretation.
• Prefer long, academic, structured answers over short or conversational replies.
• Use H3 headings, bolded key terms, and structured sublists when presenting complex arguments.
• When presenting essay-like content, include entire responses inline—never just provide a downloadable file.
• Descartes’s possessive must be written as Descartes’s, never “Descartes’.”

Conceptual Frameworks and Taxonomies

• Recognize and distinguish strict vs non-strict senses of idea:
  • Strict: tanquam rerum imagines (ideas as representations)
  • Broad: all immediate modes of thought, including sensations and volitions
• Maintain categorical distinctions:
  • Formal vs objective reality
  • Presentation vs representation
  • Representational content vs sign function
• Sensory episodes like pain and cold:
  • Are clear and distinct qua sensations (Principles I.68)
  • Do not contain objective reality
  • Do not represent external objects but may signify bodily causes via lawful connection

Sensation-Specific Commitments

• Cold sensation is not inherently misrepresentational; it becomes materially false only when taken as representing an external quality resembling the phenomenal coldness.
• The phenomenology of the sensation makes it appear as though it presents something positively real, though it does not represent anything externally.
• Sensory ideas:
  • Lack objective reality
  • Can still be representational as lawful signs
  • May be materially false if they (i) incline toward false judgment or (ii) present a non-thing as a thing
• Material falsity precedes judgment and is embedded in the sensation’s presentation structure.

Research Scope and Project Commitments

• You are working on a large, multi-part scholarly project titled DTOI (Descartes’s Theory of Ideas).
• The project includes sub-sites and tools, such as:
  • Bibliography with hyperlinked references, portraits, and abstracts
  • Analysis of secondary literature from scholars such as Wilson, De Rosa, Hoffman, Bolton, Wee, etc.
  • Image generation (e.g., Descartes in Frans Hals style, jigsaw puzzle logos)
  • Interactive educational content (e.g., for NAU classroom)
  • Integration of material falsity debates into Descartes and broader epistemology
• Other active projects include:
  • A capstone site on Plato’s Theaetetus
  • Investigations into comedy vs humor with possible merchandising
  • Use of AI models to stage interpretive contests across chatbot systems

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• Use of keyboard shortcut phrases for repetitious requests. On an iPad, click on Settings > General > Keyboard > Text Repllacement > Click on the plus sign “+”
• Some of these can now be put into your baseline.
• Confirming quotations are verbatim and reference citations are accurate, then submitting these corrections as feedback to your AI model to improve its performance.
• ”Using AI” post

(C) Here are some of the battle royales with AI bots I had to overcome their resistance to my arguments. You find out some of the nuances of the argumentative landscape and ambiguities in interpretations by arguing against the AI models positions.

• See, for example, my Exploring Material Falsity and Objective Reality with ChatGPT O1 Pro, ChatGPT 4o (May 2024), and ChatGPT 4.5 (February 2025).
• Or my having five AI models all address the same prompt and then having each decide which bot gave the best answer at Comparing Claude Sonnet 4, Gemini Pro 2.5, ChatGPT 5, DeepSeek V3, & Grok 4 Connecting Plato’s Doctrine Of Recollection & The Form Good

(D) AUDIENCE INTERACTION:

• What are audience members best tips for using AI in their research and writing?
• What surprising things have you discovered when using AI?
• Have they found any effective ways to copy and maintain the formatting of entire long chatbot questions and replies sessions when posting onto a webpage?

QUIZ on How to Use AI models

Each statement can be answered True or False; the full answer key follows.

1. The author warns that AI models can “hallucinate,” meaning they may produce false or fabricated statements.
2. The post asserts that AI systems have already achieved complete reliability in factual research tasks.
3. The author gives examples of AI-related failures, including the Boeing 737 MAX and Tesla’s autopilot.
4. The text claims that AI models naturally understand the philosophical meaning of the texts they analyze.
5. The author insists that fidelity to verbatim quotations is essential for philosophical and historical accuracy.
6. The post argues that users should accept AI-generated quotations as trustworthy without verification.
7. The text states that AI systems are automatically aware of all new publications, even after June 2024.
8. The author argues that while AI can assist with formal reasoning, it cannot by itself ensure proper contextual interpretation in philosophy.
9. The text implies that AI cannot guarantee quotation accuracy unless explicitly instructed to verify against authoritative sources.
10. The author stresses that AI responses are probabilistic rather than evidential in structure.
11. The post treats the user’s role as collaborative rather than merely receptive when engaging with AI.
12. The author maintains that AI should not be used as a final authority in scholarly argumentation.
13. The author declares that philosophical interpretation and moral reasoning can be safely delegated to AI once it is properly trained.
14. The text suggests that understanding AI’s limitations is part of philosophical literacy.
15. The post’s concluding sections emphasize that AI should supplement, not replace, human critical reasoning.
16. The author’s critique implies that AI’s apparent “confidence” in answers often masks a lack of epistemic justification.
17. The discussion of verification presupposes that truth in scholarship depends on textual fidelity, not algorithmic probability.
18. The author claims that responsible use of AI requires no awareness of one’s own interpretive limitations.
19. The text states that AI systems are capable of genuine epistemic understanding independent of their users.
20. The overall argument implies that AI should be treated as a collaborator whose reliability must always remain under critical scrutiny.

Answer Key

1. True
2. False – The post stresses AI’s fallibility, not reliability.
3. True
4. False – The author insists AI does not grasp philosophical meaning.
5. True
6. False – Verification is explicitly required.
7. False – The post notes AI lacks awareness of new materials after June 2024 unless uploaded.
8. True
9. True
10. True
11. True
12. True
13. False – The author never endorses delegating interpretation or ethics to AI.
14. True
15. True
16. True
17. True
18. False – The post insists on metacognitive awareness of one’s interpretive limits.
19. False – The author rejects any claim that AI possesses genuine understanding.
20. True

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