Understanding Student Dependency on AI

Core Finding

Among 478 Israeli higher-education students, AI literacy is a double-edged factor: its skill-based dimensions (using/understanding AI) were positively associated with reported AI dependency, while self-efficacy (both academic and AI-specific) and effort regulation were negatively associated. AI literacy alone does not protect against overreliance — it can even enable it. Self-efficacy beliefs and self-regulated learning practices appear at least as important as technical skill for balanced, intentional AI engagement.

Key Findings

• Multidimensional AI literacy operates in opposing directions. The skill-based dimensions (using/understanding AI: β = 0.404, the strongest predictor; detecting AI: β = 0.108) were positively associated with dependency. In contrast, AI self-efficacy (confidence in one's own AI competence) was negatively associated (β = −0.132), mirroring academic self-efficacy (β = −0.197). This suggests a "compensatory" mechanism: strong skills enable offloading, but strong self-efficacy buffers against it (Cognitive Offloading, Self Efficacy).
• The model explained 27.3% of variance in AI dependency (F(8,469)=22.03) — a meaningful improvement over the 22.1% from a single aggregate AI literacy score, confirming that multidimensional treatment adds explanatory value.
• Effort regulation predicted lower dependency (β = −0.134); general academic help seeking predicted higher dependency (β = 0.124); time/study management was not significant. The help-seeking measure captures human sources, so the finding does not show AI substituting for human help.
• Four student profiles emerged (K-means cluster analysis): HL (high literacy, low dependency — most adaptive), MM (moderate/moderate), LL (low/low), and HH (high literacy, high dependency). The HL profile scored highest on academic self-efficacy, time/study management, and effort regulation; HH — despite strong technical knowledge — did not lead on any self-regulation measure, suggesting reliance may undermine learner autonomy and create an illusion of competence.
• The dual structure within AI literacy (skills that enable use vs. beliefs that govern reliance) is a novel contribution to the AI-dependency literature, supporting calls to treat AI Literacy as multidimensional rather than unitary.

The compensatory self-efficacy mechanism

The central insight is that skill and confidence pull in opposite directions in predicting dependency. AI literacy curricula focused narrowly on technical skill (how to prompt, how to use tools) may inadvertently increase dependency. By contrast, curricula that also build students' sense that they can "think with AI rather than through AI" — and their general academic confidence — may protect against overreliance. The authors frame this as a shift from a technological-instrumental conception of AI literacy toward a pedagogical, behavioral, and emotional one foregrounding self-regulation, self-awareness, and learner responsibility.

Practical Implications

• Do not treat AI-literacy training as sufficient for responsible use. Fostering technical proficiency alone can raise dependency; pair it with interventions that build academic and AI self-efficacy and self-regulated learning.
• Scaffold self-regulation, not just prompting. Students high in dependency report weaker effort regulation and time management — design instructional scaffolds for effort, time management, and reflective engagement with AI outputs (process documentation, reflective dialogue, critique of AI-generated outputs) rather than only tool skills.
• Differentiate support by learner profile. Since four distinct profiles emerged with different needs, frameworks for learning and support should be adjusted to user characteristics — foundational AI skills for novice users, critical/reflective use for those already skilled.
• Interpret AI dependency as a self-regulation and motivation issue. The differences among profiles reflect disparities in self-control, academic self-efficacy, persistence, and resource management — not mainly in technological knowledge or access. Position AI as a supportive resource embedded within emotional–behavioral skills that enable conscious, critical, autonomous use.

Connected Concepts

• AI Literacy
• Cognitive Offloading
• Self Efficacy
• Self Regulated Learning
• Help Seeking
• Motivation
• Student Engagement
• Student Misconceptions AI
• Reducing AI Misuse
• Generative AI
• LLM
• Higher Ed
• AI Education

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• End Of Assessment AI Disruption Transformation 2026 — The End of Assessment? Disruption and transformation in the age of AI
• Constructing Epistemic AI Literacy Student AI Co Programming — Epistemic AI literacy in student-AI co-programming
• GenAI Thoughtless Use Self Directed Learning 2026 — GenAI and thoughtless use in self-directed learning
• AI Anxiety Strategic Regulation Writing 2026 — From AI anxiety to strategic regulation

Citation

Maizel, H., Kalman Halevi, M., Sarid, M., & Tutian, R. (2026). Understanding Student Dependency on AI: The Role of AI Literacy, Academic Self-Efficacy, and Resource Management Strategies. Education Sciences, 16(7), 1123.