Design AI decision-support systems that are transparent, context-sensitive, and hybrid-intelligence oriented¶

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

Description¶

The review derives design implications for future AI decision-support systems in higher education, stating that "the study derives design implications for the development of more transparent, context-sensitive, and hybrid intelligence decision-support systems in higher education." It highlights the open question of how alternative forms of student data visibility, attentional guidance, and interface design could support lecturer decisions currently underrepresented, such as emotional, ethical, and curriculum decisions.

Design Implications¶

Context¶

Requirements¶

• Design should address underrepresented decision domains by making less observable processes visible and guiding lecturer attention beyond measurable behavioral indicators

Constraints¶

• Current systems predominantly make behavioral student data visible, so supporting motivation, metacognition, emotions, and learning environments requires alternative data visibility and interface approaches

Target Learners¶

• Higher education lecturers

Target Learning Goals¶

• Pedagogical decision-making across instructional, feedback, assessment, emotional, ethical, and curriculum domains

Affordances¶

• Socio Technical Mechanisms Ai Lecturer Decisions

Claims¶

• AI support in higher education is concentrated in instructional, assessment, and feedback decisions, with curriculum and learning-environment decisions least supported [~W]

Related Strategies¶

• Explore hybrid assessment models combining AI conversational agent-based and face-to-face formats, with long-term study of effects on performance and well-being
• Use five conceptual propositions to guide research and professional development on teachers' diagnostic skills in AI-supported formative assessment
• Pursue symbolic, hybrid, and metaverse-based AI approaches to address gaps left by subsymbolic AI in collaborative learning

Examples¶

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Key Sources¶

• Köroğlu, M. N., Mayrhofer, J., Steinmaurer, A., Wintersberger, P., Dennerlein, S. M., & Weinhandl, R. (2026). AI-Supported lecturer decision-making in higher education: a socio-technical perspective. International Journal of Educational Technology in Higher Education. https://doi.org/10.1186/s41239-026-00623-8