Islem Rekik (2026) — ICLR HCAIR Workshop 2026.
Overview
This paper presents a striking longitudinal finding: as AI becomes a routine educational tool, students systematically revalue human intelligence (HI) over artificial intelligence (AI). Drawing on 6 years of classroom poll data (2020–2026) from 471 undergraduate and MSc computer science students, Rekik documents a cognitive shift that progresses through four phases: hype → distrust → trust → dependency.
Key Findings
Four-phase trajectory of student perception: 1. Hype (2020): Initial excitement slightly favored AI over HI in poll responses 2. Distrust: Emerging skepticism as students encountered AI limitations 3. Trust: Growing reliance on AI tools for coursework 4. Dependency: Habitual use leading to a reappraisal of what makes human intelligence valuable
Converging toward human intelligence. From 2024 onward, a consistent shift toward HI preference emerged across all MSc cohorts. By 2026:
- Technical course (ML/Deep Graph Learning): 65% preferred HI (↑12pp from 2025)
- Design-oriented course (Design Thinking for AI): 90% preferred HI (↑36pp from 2025)
This is a striking reversal from 2020, when AI was slightly favored.
Implications for AI Literacy and Learner Autonomy
The findings directly challenge the assumption that increased AI exposure leads to increased AI trust. Instead, sustained use produces a more nuanced — and more skeptical — relationship. The design-oriented course's 90% HI preference suggests that pedagogical framing matters: courses that teach students to think critically about AI (not just with AI) accelerate this cognitive shift.
This connects to contextual-sycophancy-ai-literacy, which found that AI literacy interventions alone may be insufficient to prevent over-reliance. The cognitive shift documented here suggests a longer-term developmental trajectory: epistemic recalibration happens through immersion, not instruction.
The paper's emphasis on learner autonomy and epistemic agency ties directly to genai-performance-vs-learning, which warns that AI tools can improve task performance while undermining learning. The shift toward HI preference may reflect students' growing awareness of this tradeoff.
Connections to Faculty Development
For instructors, these results suggest that AI education should explicitly surface the value of human cognition rather than treating AI as a neutral productivity tool. The ai-pedagogical-orientation framework shows that faculty AI orientation strongly predicts adoption — this paper adds that student orientation evolves dynamically and may benefit from curricular scaffolding.
Methodological Notes
- N = 471 students across technical and design-oriented courses
- Poll-based measurement of HI vs. AI valuation
- Longitudinal design spanning 6 years (2020–2026)
- Workshop paper (ICLR HCAIR), not yet peer-reviewed at a major venue
Related Pages
- generative-ai-reduced-study-time-math — 3.2M-interaction study confirms population-level cognitive shift toward AI dependency (2026)- contextual-sycophancy-ai-literacy — AI literacy intervention study showing limits of instruction-only approaches
- ai-literacy — Core page on AI literacy research and frameworks
- ai-pedagogical-orientation — Faculty AI pedagogical orientation as predictor of adoption
- genai-performance-vs-learning — Tradeoff between AI-assisted performance and genuine learning
- ai-generated-slides-student-perception — Student perceptions of AI-generated educational content
- chatgpt-programming-education-text-mining — Student experience with AI in programming education
- ai-higher-ed-bridge-gap — Higher education AI integration challenges
- generativism-learning-theory — Generativism describes the fundamental cognitive shift AI brings to learning
Citation
APA: Rekik, I. (2026). Evidence of a Cognitive Shift in AI Education: How Students Are Rethinking Human Intelligence? arXiv:2605.16292. ICLR HCAIR Workshop 2026.