AI as asset and liability: A dual-use dilemma in higher education and the SPARKE Framework for institutional AI governance
Olumide Malomo et al.
What the paper says
Artificial intelligence (AI) is rapidly transforming higher education by enhancing instructional processes, improving administrative efficiency, and enabling personalized learning experiences. However, it also introduces significant risks related to privacy, authorship, academic integrity, and institutional accountability. As adoption accelerates, universities face an urgent need to establish clear, campus-wide governance structures that define responsible use, protect institutional knowledge assets, and ensure ethical implementation. This study examined this dual-use dilemma through a conceptual document analysis, systematically reviewing and synthesizing institutional policies, academic literature, and global governance frameworks. These sources were compared to identify cross-framework patterns, recurring institutional gaps, and practical requirements for the responsible adoption of AI. Findings reveal weaknesses in academic integrity guidelines, data governance controls, authorship standards, literacy training, and mechanisms for monitoring AI-enabled systems. In response, this study introduces the SPARKE Framework, a six-component model that translates ethical principles into operational safeguards by integrating policy development, privacy protections, integrity rules, literacy initiatives, knowledge management processes, and enforcement mechanisms. The framework contributes to applied knowledge management by offering institutions a structured roadmap for aligning innovation with accountability. By emphasizing transparency, oversight, institutional learning, and responsible use, the SPARKE Framework provides universities with a scalable approach for managing AI across academic and administrative environments. It highlights broader implications for academic integrity, institutional trust, and the development of sustainable governance infrastructures that support responsible AI adoption.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.