Treat refusal diagnostically and provide non-AI pathways where AI use is not essential to the learning outcome¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article recommends that universities ask what refusal reveals about educational and professional conditions rather than how to reduce it. Student learning and assessment 'should protect legitimate refusal by providing non-AI pathways where AI use is not essential to the learning outcome', while still teaching how AI shapes disciplines and professions. Refusal may reveal defence of care, craft or integrity; covert use may signal assessment credibility problems, excessive workload or fear-inducing policy.

Design Implications¶

Context¶

Requirements¶

• Clear alternatives, transparent rules, secure tools, workload recognition and protection from covert productivity expectations to make refusal practically possible

Constraints¶

• The right to refuse is unevenly distributed: students needing language or accessibility support, casual staff and those working with others' system choices may experience refusal as a loss of opportunity

Target Learners¶

• higher education students
• academics
• professional staff

Target Learning Goals¶

• preserving authentic evidence of learning, professional agency and educational judgment when AI is introduced

Affordances¶

• Ai Refusal Spectrum Higher Education

Related Strategies¶

• Build teacher AI readiness through structured institutional support: professional development, mentoring, technical assistance, and AI literacy in teacher education

Examples¶

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Key Sources¶

• Zagami, J. (2026). AI refusal in higher education: the right to refuse, the duty to understand and the diagnostic value of non-use. Higher Education Research & Development, 45(7), 2287-2294. https://doi.org/10.1080/07294360.2026.2694409