Prioritize real-world testing of AI tools in university settings, environmental impact measurement, and integrated ethical-sustainability policies¶

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

Description¶

The review's forward-looking recommendation is that future research move beyond conceptual and pilot work by testing AI tools in real university settings, measuring their environmental impact, and developing policies that combine ethical and sustainability goals. It argues greater involvement from students, staff, and faculty is essential to ensure AI supports a fair and sustainable future for higher education.

Design Implications¶

Context¶

Requirements¶

• Sustained investment, policy alignment, and interdisciplinary collaboration to achieve AI's transformative potential in higher education.

Constraints¶

• The review notes governance frameworks in higher education often overlook environmental aspects, raising the risk of unintended ecological effects.

Target Learners¶

• higher education institutions
• students, staff, and faculty
• policymakers

Target Learning Goals¶

• responsible and sustainable AI adoption
• institutional sustainability strategy

Claims¶

• Most AI-for-sustainability initiatives in higher education remain limited in scope, exploratory, and not integrated into institutional strategies [+W]

Related Strategies¶

• Pursue sustainable AI integration in PBL through attention to design, ethics, and policy
• Higher education leaders should develop more inclusive and future-oriented GenAI policies integrating social equity, interdisciplinary experimentation, and sustainability considerations
• Future improvements: real-world user studies, advanced student modeling, and spaced repetition
• Prioritize inclusive-by-design architectures, interoperable agent frameworks, human-AI collaboration models, and real-world implementation studies
• Prioritise equity-centred approaches, methodological diversity, contextual specificity, implementation science and interdisciplinary collaboration in GenAI research
• Future AIHEd research should give greater attention to ethics, methodological rigour, context, and interdisciplinary collaboration
• Pursue future research on ChatGPT in higher education to fill the gaps the scoping review identifies
• Prioritize construct-to-data mappings, multimodal validation, ethical implementation, and cross-disciplinary collaboration for domain-specific adaptive systems

Examples¶

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Key Sources¶

• Ben Kei Daniel, Nataliya Podgorodnichenko and Sarah Carr. (2026). A scoping review of AI for sustainability and sustainable AI in higher education. Discover Computing. https://doi.org/10.1007/s10791-025-09861-2