Design AI literacy curricula that foster self-efficacy beliefs alongside technical skills to protect against overreliance¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article recommends that AI literacy curricula should not focus narrowly on technical skills, since "AI literacy curricula that focus narrowly on technical skills (how to prompt, how to use AI tools effectively) may inadvertently increase dependency". Instead, curricula should also foster self-efficacy beliefs about AI — students' sense that they can think with AI rather than through AI — and build general academic self-efficacy, which the authors argue may be at least as protective as teaching students to use AI well. The recommendation rests on the regression findings that skill-based literacy positively predicted dependency while both self-efficacy beliefs negatively predicted it.

Design Implications¶

Context¶

Requirements¶

• Curricula must address self-efficacy beliefs (AI-specific and general academic), not only technical operation of AI tools

Constraints¶

• Grounded in cross-sectional correlational findings; the article does not report a test of such curricula

Target Learners¶

• higher education students

Target Learning Goals¶

• balanced and intentional AI engagement
• independent academic effort

Affordances¶

• Three Mechanism Integrative Model Ai Dependency

Related Strategies¶

• Enhance students' AI literacy while alleviating excessive academic stress to reduce over-dependence on generative AI
• Differentiated interventions integrating AI literacy training, psychological capital cultivation, and professional identity reinforcement

Examples¶

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Key Sources¶

• Hilit Maizel, Maya Kalman Halevi, Miri Sarid, and Rony Tutian. (2026). Understanding Student Dependency on AI: The Role of AI Literacy, Academic Self-Efficacy, and Resource Management Strategies. Educ. Sci. 2026, 16, 1123. https://doi.org/10.3390/educsci16071123