On October 20, a few dozen San Diego State students marched to their student union and asked the California State University system to cancel its $39 million contract with OpenAI. Two days later, faculty and students rallied in the rain outside a CSU symposium in San José, demanding fair pay and protection against AI replacing instruction. The same week, a global survey of business school deans found AI to be the area where their priorities most outrun their preparation. The pattern from October 18 to 24 is consistent: institutions are expanding AI faster than they are building the leadership capacity, faculty support, and evidence needed to manage it. The leadership question is whether institutions are developing that capacity as deliberately as they are expanding AI use.
“This investment allows us to accelerate the work of updating curriculum, supporting our students and providing Michigan employers with the skilled employees they need.”
Anthony W. Anderson, Interim President, Davenport University (Lyon, 2026)
Business school deans rank AI high but rate their own readiness low
The Details
AACSB International’s 2026–27 Deans Survey, released October 19, drew responses from 558 leaders, including 522 deans or equivalent heads and 36 interim deans, across 75 countries and territories. Deans ranked AI in education and research second in priority among 49 typical dean activities, and it showed the largest gap of any activity between priority and self-rated proficiency, regardless of region or experience. Across eight dimensions of institutional future readiness, AI had the widest gap: 4.37 for strategic priority versus 2.91 for preparedness on a five-point scale, a difference of 1.46, and the only dimension with average preparedness below 3. Seventy-four percent named AI readiness among their schools’ three biggest challenges over the next five years. Meanwhile, 84% reported limited, very limited, or no time for professional development, and 34% named budgets, finances, or resources among their top challenges (AACSB International, 2026; Bernama, 2026; Bleizeffer, 2026).
Why it Matters
These are self-assessments, not audits, so they do not show that any particular school lacks capability. They do show a structural problem. Deans are expected to redesign curricula, support faculty, protect standards, and respond to employers on AI, yet most have little time to build their own expertise, and AI is the one area where they rate themselves below the midpoint. If leaders cannot get that time, faculty are unlikely to get it either. Use the survey as a starting point for an honest readiness review covering governance, faculty capability, curriculum, assessment, employer partnerships, and the resources implementation will actually take.
Where AI help ends and student work begins: six principles from 31 studies
The Details
Jiayu Tie and MariCarmen Gil Ortega of the University of Bath and Dan Zhao of the British Council reviewed 31 English- and Spanish-language higher education studies published from January 2022 to June 2026 on responsible student use of generative AI in academic writing. They charted 192 records describing AI writing practices and 122 describing educational approaches. Six principles recurred: target the learning objective, preserve human agency, verify AI output, practice transparency and accountability, protect authorship and the student’s own voice, and respect privacy, consent, and fairness. These judgments happen at the level of each writing task, but course design and institutional guidance either sustain or undermine them. Most educational approaches were pedagogical, and most evaluations relied on short-term outcomes and self-reports, so evidence on long-term learning remains thin (Tie et al., 2026).
Why it Matters
The review moves the conversation past whether students may use AI to which intellectual work they must keep doing themselves. The same help can be fine in one course and harmful in another. Asking AI to fix grammar may be appropriate where the goal is disciplinary knowledge, and a problem where the goal is learning to construct language independently. Faculty need policies that name the responsibilities students retain, and programs need consistency across courses so those expectations are clear. Check whether your policies explain how students show independent understanding, verify information, keep authorship, and stay accountable for what they submit.
Policy & Governance
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UK science advisers call for an AI sovereignty plan
The UK Council for Science and Technology published advice on October 19, first sent to the Minister for AI in July, calling for a UK AI Sovereignty Plan. It recommends clear governance and coordination for sovereignty decisions, investment now in long-term domestic foundations including skills, data, and public trust, and maintaining trusted access to leading AI models while building selected UK capability (Council for Science and Technology, 2026). This is advice, not adopted government policy. For research universities in any country, it raises a parallel question: how dependent are teaching, research, and administration on externally controlled AI systems? Contracts should protect research independence, intellectual property, data access, and the ability to change providers.
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Ecuador study finds one formal AI policy among seven universities
Abel Suing and colleagues combined interviews with 14 lecturers and 8 students, a survey of 200 social science students, and an analysis of AI regulations from seven Ecuadorian universities. Student AI use was moderate to high. Only one of the seven universities had a policy formally designated as an AI policy, and existing guidance emphasized ethics while largely leaving out professional development and discipline-specific competencies. Students’ use of AI was not related to their ethical concerns; many use it while still worrying about plagiarism and dependence. The samples were non-probability, and the authors note that one survey scale had very low reliability (Suing et al., 2026). Access does not teach expectations. Students and faculty need clear policy, worked examples, faculty preparation, and guidance tailored to each discipline.
Programs, Research & Infrastructure
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Davenport University wins $3 million to build AI into programs and student services
Davenport University announced October 19 a five-year, $3 million Strengthening Institutions Program grant from the U.S. Department of Education, $600,000 a year from October 2026 through September 2031. The project, STAR (Strengthening Through Advanced Resources), led by Provost Gilda Gely, will work with employers and workforce advisory boards to identify AI skills by profession; expand AI certificates, microcredentials, digital badges, and experiential learning; build predictive analytics and AI tools for advising, planning, tutoring, and coaching; launch a STAR AI Faculty Fellows Program; and set university-wide guidelines on privacy, transparency, academic integrity, fairness, and human-centered decision-making. It is expected to reach more than 4,000 students (Lyon, 2026). Davenport links workforce preparation with the systems that support student success. Its results should be measured in demonstrated student competencies, faculty readiness, employer feedback, and academic outcomes.
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Emory opens Claude for Education to its whole community
Since October 19, Emory students, faculty, and staff can request access to Claude for Education as part of the university’s AI.Humanity initiative. Standard licenses are free; premium licenses with higher usage cost $50 per license per month, funded by departments. The offering includes Projects, Artifacts, and a Learning Mode for guided reasoning; Claude Code and Claude Cowork are excluded for now and being evaluated in limited pilots. Users may not enter protected health information, controlled unclassified information, identifiable student information, passwords, or other restricted data, and they are responsible for verifying AI output. People with existing personal subscriptions tied to Emory email are told to keep using them for now while IT plans migration (Emory University, 2026). Like Georgetown’s pilot last week, Emory is drawing clear lines around data, features, and personal accounts. Access still does not prove educational value; that has to be measured.
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Optional AI-patient practice did not change medical students’ confidence
Researchers at Amsterdam UMC and the University of Amsterdam gave first-year medical students optional access to a GPT-4o virtual patient before their first standardized-patient communication session. Of 383 eligible students, assigned by class schedule, 180 completed the questionnaire; of the 96 respondents in the access group, 49 used the tool, mostly once. After correcting for multiple comparisons, there were no significant differences in nervousness, preparedness, self-efficacy, or perceived competence. Most users rated it positively. The study was underpowered, used one text-only case, relied on self-reports, and made use voluntary, so it does not show that AI simulation cannot work (Jongkind et al., 2026). It does show that a tool students like is not the same as a tool that improves performance. Programs should test simulations against performance in real professional encounters.
Other
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“Simulated innovation”: when AI looks like change but isn’t
In a conceptual analysis published October 23, Diana Carolina Fajardo-Ramos and Andrés Chiappe of Universidad de La Sabana and Sandra Martínez-Pérez of the University of Seville describe “simulated innovation”: technology adoption that looks modern but leaves learning goals, assessment, teacher judgment, and student agency largely unchanged, often measuring success by usage rather than learning. They argue education should set the agenda for AI rather than react to it, and that teacher education is a key place to build that capacity, through sustained development rather than one-off workshops (Fajardo-Ramos et al., 2026). This is an argument, not an outcome study. Its practical point for leaders is simple: decide what students must learn and demonstrate before deciding which AI belongs in the curriculum.
Do It Now Checklist
Betting On: Institutional AI Readiness
This week’s stories describe one gap from several angles. Deans rank AI near the top of their agenda and their own preparation near the bottom. Cal State students question what a $39 million contract delivers, and faculty ask who carries the work. Researchers find students need clearer boundaries, universities need clearer policies, and a well-liked AI tool did not change outcomes. Technology adoption is moving faster than the preparation of the people responsible for it. Institutions that invest in leadership development, faculty support, clear governance, and real evaluation will be the ones able to show whether AI is delivering educational value.
With Inspiration Moments, we share motivational nuggets to empower you to make meaningful choices for a more fulfilling future. Remember: “Life happens for you, not to you, to live your purpose.”
Respectfully,
Dr. Lynn Austin