Design AI-enhanced learning management systems that explicitly target engagement across cognitive, emotional, behavioral, and social domains¶

Strategy · All strategies
Evidence · 5 claims (3 for, 1 mixed, 1 against) · 1 study (1 review), q2 · 0 of 1 report an effect size · 5 claims rest on one study

Description¶

The review proposes its synthesis as a design foundation: "this work offers a foundation for future AI-enhanced learning management systems designed primarily to enhance learner engagement across cognitive, emotional, behavioral, and social domains". Designers should draw on the identified positive factors (personalization, real-time feedback, emotion-aware systems, chatbot support) while building in safeguards for the identified challenges (privacy, bias, over-reliance, usability).

Design Implications¶

Context¶

Requirements¶

• AI features must be easy to use and visually intuitive, since confusing interfaces reduce learner engagement.

Constraints¶

• The review warns that concerns around data privacy, algorithmic bias, over-reliance on automation, and system usability persist and must be addressed.

Target Learners¶

• higher education students in online and blended courses

Target Learning Goals¶

• cognitive, emotional, behavioral, and social engagement
• learner satisfaction

Claims¶

• AI-driven personalized and adaptive learning pathways support learner engagement and satisfaction in online courses [+W]
• Ethics, privacy, bias, over-reliance, accuracy, usability and cost are key challenges AI use poses for learner satisfaction and engagement [~W]
• Easy access to AI-generated answers may reduce learners' motivation to engage deeply with material [-W]
• Chatbot support systems in online learning are associated with higher learner satisfaction and engagement when perceived as useful [+W]
• Eight AI-driven factors positively influence learner satisfaction and engagement in online higher education [+W]

Related Strategies¶

• Upgrade retrieval to Agentic RAG and adopt context-aware emotive narration to reduce cognitive load
• Recommendations for future CA development: educator design templates, multilingual support, and theory-informed pedagogical integration

Examples¶

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Key Sources¶

• Ana Katalinic, Vanja Slavuj, Danijela Jaksic. (2026). Artificial Intelligence in Online Education: A Systematic Review of Its Impact on Learner Engagement and Satisfaction. Educ. Sci. 2026, 16, 389. https://doi.org/10.3390/educsci16030389