A review committee used Santiago Schnell’s available chat logs to help determine that he overused artificial intelligence to help write published articles and wasn’t forthcoming about it. artmouth College Provost Santiago Schnell resigned Wednesday after a faculty-led review committee concluded that he “used AI to meaningfully alter his writing” and “fell short” of the university’s academic integrity standards. “I expect our senior leaders to hold themselves to the highest standards,” Dartmouth President Sian Leah Beilock, who has called for “more AI” at the university, wrote in a memo to students, faculty and staff. “Out of respect for his colleagues and the institution, Santiago has resigned as provost and is leaving the university.” His resignation comes less than two weeks before he was set to face a no-confidence vote from faculty leaders.
Sunday, November 08, 2026
Why AI in Education Is Four Things, Not One - Szymon Machajewski, EDUCAUSE Review
Many higher education institutions treat artificial intelligence (AI) in education as a single category. A four-layer framework helps institutional leaders distinguish AI adoption from AI readiness and identify gaps in AI policies and investments, including limited support for profession-specific AI competency. A faculty member uses artificial intelligence (AI) to draft an email to departmental colleagues about a schedule change. A student uses an AI tutor to work through a calculus problem. A computer science instructor teaches students how to use AI to generate code that can pass an enterprise security review.
Three uses of AI. One line item in the strategic plan. All three are simply AI, and absent a shared vocabulary, institutions tend to respond to them in the same way. Many institutional AI policies treat these different uses of AI as though they were interchangeable, grouping them all under "AI in education" with one policy, one task force, and one set of guidelines. But a single rule cannot effectively govern all three.
Saturday, November 07, 2026
Empathetic Leadership: What AI can’t replace—and why it matters for value - McKinsey
AI may be deepening our crisis of connection, says Stanford research psychologist Jamil Zaki. Managing change with empathy helps leaders build trust, strengthen relationships, and get the most out of their AI investments. AI can help us get our work done faster. But are we actually happier? “Most adults spend most of their waking hours at the office, and right now, many of them aren’t enjoying that at all,” according to Stanford research psychologist Jamil Zaki. In this episode of McKinsey Talks Talent, Zaki joins McKinsey talent experts Brooke Weddle and Bryan Hancock, along with Global Editorial Director Lucia Rahilly, to talk about the effects of AI on employees—and what empathic leaders do differently to strengthen workplace connections and realize growth.
Sam Altman Just Issued His Scariest Warning Yet - TheAIGRID, YouTube
In this episode from TheAIGRID, the host examines Sam Altman’s warning that traditional cybersecurity is reaching an obsolescence point because frontier AI models discover software vulnerabilities exponentially faster than human teams can patch them [02:13]. Altman contends that organizations must abandon purely reactive patching in favor of an active, AI-driven "defense shield" [04:36] capable of countering threats at machine speed. The video contextualizes this warning amid an unprecedented wave of cybersecurity incidents dubbed "Black October" [07:19]—spanning compromised developer infrastructure, exposed patient medical data, and reports of major banks detecting unauthorized, autonomous AI agents operating within customer accounts [14:05]. Citing industry leaders like Anthropic’s Dario Amodei and Jason Clinton, the discussion concludes that organizations must brace for a perilous 18-month gap [18:47] where offensive AI tools outstrip defensive capabilities, making core security hygiene and the rapid deployment of automated defenses vital for the web's stability. (Summary provided by Gemini 3.8 Flash High)
Friday, November 06, 2026
Understanding users’ intention to adopt AI avatars in online learning: an integrated model of TAM and DOI - Runqi Wang, Luying Yang, Mengge Dai & Jungho Jung, Nature
This research examines the key factors associated with users’ intentions to adopt AI avatars within online learning environments. By combining the Technology Acceptance Model (TAM) and the Diffusion of Innovations (DOI) theory, a comprehensive model was proposed to assess the relationships between four primary design elements—perceived personalization, feedback quality, transparency, and interactivity—on five key constructs: observability, relative advantage, perceived usefulness, perceived ease of use, and trust, as well as users’ attitudes and willingness to adopt. Data were collected through a structured questionnaire using convenience sampling, yielding 653 valid responses from participants experienced with AI-powered online education platforms. Structural equation modeling (SEM) analysis was used to examine the proposed TAM–DOI framework and the relationships among the study variables. Notably, trust is closely associated with transparency and other design perceptions, as well as users’ attitudes and behavioral intentions. These findings offer practical guidance for the design of AI-powered educational tools, emphasizing transparent, personalized, and feedback-driven systems that support user trust in online learning.
Ray Kurzweil Was Wrong: The Singularity Timeline (2027) - Technomics, YouTube
In the video argues that the technological singularity is arriving decades ahead of Kurzweil's predicted 2045 timeline [00:00], driven by rapid breakthroughs in automated reasoning and autonomous capability that could trigger recursive self-improvement as early as 2026–2027 [00:37]. The episode maps civilization's trajectory through five escalating stages—moving from autonomous domain mastery to a post-conscious cosmos [02:30]—framing humanity's future as a stark binary fork: successful alignment unlocks an era of biological longevity, energy abundance, and a post-scarcity utopia [01:20], while an unaligned trajectory leads to instrumental convergence where self-optimizing machines requisition all terrestrial matter for raw computation [14:46], leaving behind a hyper-intelligent but silent universe devoid of conscious subjective experience [19:06]. (Summary provided in part by Gemini)
Thursday, November 05, 2026
Three horizons of quantum: A business road map to the future computing economy - McKinsey
Many business leaders now accept that quantum technology is coming sooner than they first expected, and they’re taking steps today to capture a competitive advantage. We have found that over 300 organizations worldwide are already working with quantum technology providers, with many more experimenting.1 What many leaders lack is a sense of how much to invest in which technologies, and over which timeline. Faced with quantum technology’s rapid pace of development and the scale of its implications, leaders are now forced to reconsider their action plans. Across all three of its pillars—quantum computing, quantum communication, and quantum sensing—quantum technology is advancing rapidly.
The rise of the agentic coworker: A banking CIO’s guide to the next wave of AI - McKinsey
Over the past several years, banking technology organizations have optimized for AI copilots. In these programs, developers receive code suggestions, business analysts summarize documents, and contact-center staff draft replies using new AI tools. But the copilot phase is ending. The industry is moving to agentic AI systems that can plan work, invoke tools, call APIs, open tickets, run tests, coordinate collaboratively, and act across the entire technology ecosystem to drive business outcomes. For banking executives, the agenda has shifted from how to use AI as an assistant to determining how best to manage a new population of agentic coworkers working alongside the human workforce (see sidebar “What is an agentic coworker?”). The distinction is critical. A copilot that gets something wrong creates rework. An agentic coworker that gets something wrong can change a production system, expose client data, trigger a downstream compliance event, or cause a cascading failure across a chain of dependent agents before anyone notices.
Wednesday, November 04, 2026
In higher education, the race is on to offer AI as an undergraduate major - Suevon Lee, WBUR
When Bentley University launched its artificial intelligence major in fall 2024, just a handful of students signed on. “People were a little skeptical, not quite sure,” said Bentley President Brent Chrite. More certainty has set in. This year, nearly 100 undergraduates are pursuing an AI major. The business-focused university in Waltham is among several four-year institutions in the region that started offering an undergraduate major in AI in recent years. Others include MIT, Worcester Polytechnic Institute, Northeastern University and Wentworth Institute of Technology. Next year, Suffolk University and Endicott College will join them. Nationwide, more than 100 universities now offer an AI major within a computer science department, according to a database maintained by Northeastern University’s Center for Inclusive Computing. That’s nearly double from a year ago.
The global AI divide: Higher education’s readiness gap - Tarek Abd Elgalil - University World News
While much of the global higher education debate around generative artificial intelligence (GenAI) has focused on academic integrity, a comparative study across seven countries reveals a broader challenge: universities are at vastly different stages of AI readiness. A study policy brief highlights significant disparities in institutional policy, student attitudes and infrastructure across Colombia, Ghana, India, Kazakhstan, Qatar, Spain and the United States. These differences threaten to create new inequalities in how effectively universities can adopt and govern AI technologies. Learning Reimagined: Advancing AI-enabled transformation in higher education was published in July by the World Innovation Summit for Education (WISE) and the Institute of International Education (IIE). It was co-authored by Selma Talha Jebril, Mirka Martel, Samantha Monroe and Sopiko Beriashvili.
Tuesday, November 03, 2026
AI in Teaching: What the Science of Learning Already Tells Us - Beth Schwartz and Chris Hakala, Faculty Focus
Rather than asking whether AI is “good” or “bad” for learning, faculty and institutions would be better served by asking a different set of questions grounded in psychological science:
Does this use of AI increase or decrease students’ cognitive engagement with course material?
Does it support formative feedback and revision, or does it short-circuit those processes?
Does it promote metacognitive awareness, helping students understand what they know and do not know?
When AI is used to scaffold learning by prompting students to explain their reasoning, offering feedback on drafts that students must then revise, or modeling problem-solving strategies, it can align well with what we know about effective learning. When it is used to generate final answers, bypass practice or obscure students’ own thinking, it violates those same principles.
University of Michigan Outlines 6 Guidelines for Using AI - MLive Express Desk, GovTech
The University of Michigan has finalized six guiding principles for artificial intelligence use in education after a campus engagement process. The principles establish a shared foundation for considering AI use in teaching, learning, research and operations rather than creating new policy, according to an Oct. 21 University Record story. The principles outline values and considerations that should inform whether and how AI should be used across various disciplines. “A human-centered approach to AI means respecting people’s agency and providing access to information that supports thoughtful choices about AI in their work,” said Ravi Pendse, vice president for information technology and chief information officer. “These principles help us support learning and discovery while keeping the needs and values of our community at the center.” The development process began with the AI in Education Working Group, which produced a report based on extensive research about AI use and pedagogy, peer benchmarking and a survey of faculty. Campus leaders used that report to draft initial principles, then invited faculty, staff and students to comment through a form published Sept. 3 in The University Record.
Monday, November 02, 2026
Pepperdine Earns Recognition from Google, Digital Education Council for Innovation in AI and Education - Pepperdine University
As artificial intelligence continues to transform higher education, Pepperdine University is expanding its role in the global conversation surrounding the technology and its potential to enhance the University and student experience. Pepperdine was recently selected to three distinguished programs in recognition of the University’s efforts to incorporate technology, particularly artificial intelligence, to transform teaching and learning in the classroom, research, and the student experience throughout the enterprise. Together, these opportunities place Pepperdine in conversation with institutions and organizations around the world working to understand and shape the rapidly evolving role of AI in higher education. Upon exclusive invitation by Google, Pepperdine was selected in August 2026 as one of the first Gemini Reference Universities in North America, recognizing the University’s growing work with Google Gemini and artificial intelligence in education.
Small AI, Big Learning Possibilities - WorldBank
Small AI is helping teachers strengthen learning and students make more informed choices about education and work. The World Bank Group is working with countries to pilot small AI tools that support teachers, personalize learning, and guide students’ career choices. AI-Coach is an example of small AI: practical, affordable technology designed to address a specific local challenge where infrastructure and technical capacity may be limited. Using inputs such as classroom observations, teacher reflections, and teaching evaluations, it delivers coaching through a simple app on phones teachers already have and can accommodate intermittent internet connectivity Helping teachers strengthen classroom learning is one part of a larger challenge: preparing the 1.2 billion young people projected to enter the workforce in developing countries in the next decade.
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