Use the AI Fatigue Model to adjust task design, pacing, and scaffolding at points where AI-related pressure is likely to increase¶

Strategy · All strategies
Evidence · 1 claim (1 for) · 1 study (1 qualitative), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

The article recommends that educators use the stage-based model diagnostically: identify points in the academic task cycle where cognitive or motivational pressure is likely to increase, then adjust task design, pacing, and scaffolding accordingly. A concrete example the authors give is "building structured reflection activities that prompt students to evaluate their own reasoning independently of AI output." Institutions can similarly design AI literacy programs addressing the psychological and motivational pressures of sustained AI use.

Design Implications¶

Context¶

Requirements¶

• Educators need familiarity with the five dimensions and stage progression of the AI Fatigue Model to locate pressure points in their task cycles.

Constraints¶

• The model is exploratory and validated only in Philippine higher education contexts; the authors call for cross-contextual validation.

Target Learners¶

• university students using AI tools in academic tasks

Target Learning Goals¶

• sustained independent reasoning and regulated, intentional academic AI engagement

Affordances¶

• Ai Fatigue Model Stage Based Framework

Claims¶

• Cognitive Overload is the most frequently reported dimension of AI fatigue, driven by output volume and repeated verification [+W]

Related Strategies¶

• Use a mixed-methods assessment sequence: diagnostic inventory, structured verification tasks, and discipline-specific critique or workflow-design assignments
• Accommodating Processing Speed Challenges

Examples¶

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Key Sources¶

• Miranda, J. P. P., Parreño, E. B., & Rivera, J. G. (2026). Defining AI Fatigue in Academic Contexts: Dimensions, Indicators, and a Stage-Based Model Using Grounded Theory. International Journal of Learning, Teaching and Educational Research, 25(5), 91–107. https://doi.org/10.26803/ijlter.25.5.5