Evidence of a Cognitive Shift in AI Education: How Students Are Rethinking Human Intelligence?

• URL: http://arxiv.org/abs/2605.16292v1
• Date: Tue, 14 Apr 2026 14:10:12 GMT
• Title: Evidence of a Cognitive Shift in AI Education: How Students Are Rethinking Human Intelligence?
• Abstract summary: This paper presents a longitudinal analysis of classroom poll responses collected between 2020 and 2026 in AI-focused undergraduate and MSc courses in computer science.<n>Data from 471 students across technical courses and design-oriented courses reveal four recurring phases: hype, distrust, trust, and dependency.<n>We conclude by reflecting on this cognitive shift from favoring artificial intelligence toward prioritizing human intelligence.
• Score: 6.633187577087704
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Perceptions of intelligence shape how learners evaluate and rely on artificial intelligence (AI) systems. Despite rapid advances in AI capabilities, the impact of sustained exposure to these tools on students' valuation of human intelligence (HI) relative to AI remains underexplored. This paper presents a longitudinal analysis of classroom poll responses collected between 2020 and 2026 in AI-focused undergraduate and MSc courses in computer science. Data from 471 students across technical courses (such as Machine Learning and Deep Graph Learning) and design-oriented courses (such as Design Thinking for AI) reveal four recurring phases: hype, distrust, trust, and dependency. While early responses in 2020 slightly favored AI, a consistent shift toward HI emerged from 2024 onward across all MSc cohorts. By 2026, preference for HI reached 65 percent in a technical course (a 12 percentage-point increase from 2025) and 90 percent in a design-oriented course (a 36 percentage-point increase). These findings suggest a gradual reappraisal of human intelligence as AI becomes a routine tool, with implications for learner autonomy and epistemic agency. We conclude by reflecting on this cognitive shift from favoring artificial intelligence toward prioritizing human intelligence.

Related papers

• ASI-Bench: At the Dawn of Artificial Superintelligence [46.937202634067766]
  ASI-Bench is the first benchmark to jointly evaluate AI systems' capabilities of innovative exploration and autonomous scientific execution.<n>It contains 60 project-level research tasks across 11 scientific domains and progressively reduces methodological guidance.<n>Across 18 state-of-the-art agent--model configurations, the average score drops from 50.91 with full methodological guidance to 29.10 with only the method specified.
  arXiv  Detail & Related papers  (2026-08-18T01:59:00Z)
• Reshaping Undergraduate Computer Science Education in the Generative AI Era [85.78707817763434]
  Generative AI represents a turning point for Computer Science (CS) education.<n>To keep pace with advances in AI technology, CS curricula should consider a shift toward understanding and verifying AI-generated artifacts.<n>We believe that to effectively prepare future computer science graduates, capable of creating, solving problems, and managing, as well as co-creating, artifacts with AI.
  arXiv  Detail & Related papers  (2026-05-02T05:26:24Z)
• AI Researchers' Views on Automating AI R&D and Intelligence Explosions [7.10975129848806]
  Many leading AI researchers expect AI development to exceed the transformative impact of all previous technological revolutions.<n>We interviewed 25 leading researchers from frontier AI labs and academia.
  arXiv  Detail & Related papers  (2026-02-13T23:24:05Z)
• Can AI Chatbots Provide Coaching in Engineering? Beyond Information Processing Toward Mastery [1.4837859618020621]
  Engineering education faces a double disruption: traditional apprenticeship models that cultivated judgment and tacit skill are eroding, just as generative AI emerges as an informal coaching partner.<n>This convergence rekindles long-standing questions in the philosophy of AI and cognition about the limits of computation, the nature of embodied rationality, and the distinction between information processing and wisdom.<n>We synthesize critical perspectives from decades of scholarship on expertise, tacit knowledge, and human-machine interaction, situating them within the context of contemporary AI-driven education.<n>We propose a multiplex coaching framework that integrates human wisdom within expert-in-the-loop models, preserving the depth
  arXiv  Detail & Related papers  (2026-01-07T08:28:47Z)
• A Definition of AGI [208.25193480759026]
  The lack of a concrete definition for Artificial General Intelligence obscures the gap between today's specialized AI and human-level cognition.<n>This paper introduces a quantifiable framework to address this, defining AGI as matching the cognitive versatility and proficiency of a well-educated adult.
  arXiv  Detail & Related papers  (2025-10-21T01:28:35Z)
• Bridging Minds and Machines: Toward an Integration of AI and Cognitive Science [48.38628297686686]
  Cognitive Science has profoundly shaped disciplines such as Artificial Intelligence (AI), Philosophy, Psychology, Neuroscience, Linguistics, and Culture.<n>Many breakthroughs in AI trace their roots to cognitive theories, while AI itself has become an indispensable tool for advancing cognitive research.<n>We argue that the future of AI within Cognitive Science lies not only in improving performance but also in constructing systems that deepen our understanding of the human mind.
  arXiv  Detail & Related papers  (2025-08-28T11:26:17Z)
• Evaluating Intelligence via Trial and Error [59.80426744891971]
  We introduce Survival Game as a framework to evaluate intelligence based on the number of failed attempts in a trial-and-error process.<n>When the expectation and variance of failure counts are both finite, it signals the ability to consistently find solutions to new challenges.<n>Our results show that while AI systems achieve the Autonomous Level in simple tasks, they are still far from it in more complex tasks.
  arXiv  Detail & Related papers  (2025-02-26T05:59:45Z)
• Aligning Generalisation Between Humans and Machines [74.120848518198]
  AI technology can support humans in scientific discovery and forming decisions, but may also disrupt democracies and target individuals.<n>The responsible use of AI and its participation in human-AI teams increasingly shows the need for AI alignment.<n>A crucial yet often overlooked aspect of these interactions is the different ways in which humans and machines generalise.
  arXiv  Detail & Related papers  (2024-11-23T18:36:07Z)
• Perceptions of Sentient AI and Other Digital Minds: Evidence from the AI, Morality, and Sentience (AIMS) Survey [0.0]
  One in five U.S. adults believed some AI systems are currently sentient, and 38% supported legal rights for sentient AI.<n>The median 2023 forecast was that sentient AI would arrive in just five years.<n>The development of safe and beneficial AI requires not just technical study but understanding the complex ways in which humans perceive and coexist with digital minds.
  arXiv  Detail & Related papers  (2024-07-11T21:04:39Z)
• Visions of a Discipline: Analyzing Introductory AI Courses on YouTube [11.209406323898019]
  We analyze the 20 most watched introductory AI courses on YouTube. Introductory AI courses do not meaningfully engage with ethical or societal challenges of AI. We recommend that introductory AI courses should highlight ethical challenges of AI to present a more balanced perspective.
  arXiv  Detail & Related papers  (2024-05-31T01:48:42Z)
• Lifelong learning challenges in the era of artificial intelligence: a computational thinking perspective [0.0]
  The rapid advancement of artificial intelligence (AI) has brought significant challenges to the education and workforce skills required to take advantage of AI for human-AI collaboration in the workplace. This paper provides a review of the challenges of lifelong learning in the era of AI from a computational thinking perspective.
  arXiv  Detail & Related papers  (2024-05-30T08:46:11Z)
• Forecasting AI Progress: Evidence from a Survey of Machine Learning Researchers [0.0]
  We report the results from a large survey of AI and machine learning (ML) researchers on their beliefs about progress in AI. In aggregate, AI/ML researchers surveyed placed a 50% likelihood of human-level machine intelligence being achieved by 2060. Forecasts of several near-term AI milestones have reduced in time, suggesting more optimism about AI progress.
  arXiv  Detail & Related papers  (2022-06-08T19:05:12Z)
• Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
  In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
  arXiv  Detail & Related papers  (2021-06-25T22:31:55Z)
• Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of AI/AGI Using Multiple Intelligences and Learning Styles [95.58955174499371]
  We describe various aspects of multiple human intelligences and learning styles, which may impact on a variety of AI problem domains. Future AI systems will be able not only to communicate with human users and each other, but also to efficiently exchange knowledge and wisdom.
  arXiv  Detail & Related papers  (2020-08-07T21:00:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.