Embed ethical inquiry into AI-supported instruction by having learners compare AI-generated texts with peer-reviewed sources¶

Strategy · All strategies
Evidence · no claims cited

Description¶

This strategy positions AI use inside a critical-comparison activity: learners evaluate AI-generated output against peer-reviewed sources to build critical awareness and reduce misuse. The review reports that Wood and Moss (2024) "called for embedding ethical inquiry into AI-supported instruction by encouraging learners to compare AI-generated texts with peer-reviewed sources, thus promoting critical awareness and reducing the risk of misuse". It addresses the review's broader finding that ethics and academic-integrity concerns are rarely addressed in AIHE studies.

Design Implications¶

Context¶

Requirements¶

• Access to both generative AI tools and credible peer-reviewed source materials
• Instructor framing that makes the comparison an explicit ethical-inquiry task

Constraints¶

• Recommended by a cited study (Wood and Moss, 2024) rather than tested within this review

Target Learners¶

• higher education students using generative AI tools

Target Learning Goals¶

• critical awareness of AI output, academic integrity, ethical use of AI

Related Strategies¶

• Engage students in active learning assignments using generative AI with structured reflection and critique of AI outputs
• Embed AI in guided accept/reject/justify workflows, teach digital critical thinking explicitly, and integrate institutional authorship standards
• Embed GenAI literacy and ethical use directly in nursing curricula rather than prohibiting it
• Integrate AI into university physics with instructional structure, task alignment, and critical evaluation of AI outputs
• Embed AI as a learning partner and redesign assessments for AI-rich contexts
• Embed GenAI-focused reflection within self-assessment as a pedagogical strategy for ethical inquiry
• Embed explicit AI ethics instruction early and use scenario-based, guidance-oriented teaching
• Embed explicit AI-integrity practices such as ethics lessons, usage pledges, and AI-output marking into science instruction

Examples¶

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Key Sources¶

• Long DY, Wang S, Md Rashid S and Lu XT. (2026). Artificial intelligence in higher education: a systematic review of its impact on student engagement and the mediating role of teaching methods. Front. Educ. 10:1648661. https://doi.org/10.3389/feduc.2025.1648661