Motivational Ergonomics in Gamified and Artificial Intelligence-Supported Learning: An ARCS Study with Implications for Workplace Training

Synthesis: Speranza (2026) examines learner Motivation across two gamified learning conditions in higher education — conventional gamification (GAM1) and gamification with conditioned progression supported by artificial intelligence functions (GAM2/PC-AI) — through the ARCS model (attention, relevance, confidence, satisfaction). Using valid ARCS responses from 47 students (GAM1) and 51 students (GAM2/PC-AI), Welch independent-samples t-tests found no statistically significant differences between conditions on any ARCS dimension or the overall score. Exploratory item-level analyses suggested a differentiated pattern: the AI-supported condition was perceived as stronger in feedback/support and adaptive repetition, while conventional gamification scored higher on interest, clarity, sustained attention, and emotional involvement. The author proposes ARCS as a cognitive-ergonomic lens for evaluating whether technology-mediated environments are motivationally sustainable and transferable to workplace training.

Key Findings

1. No statistically significant differences between conventional and AI-supported gamification on attention, relevance, confidence, satisfaction, or overall ARCS score.
2. Valid ARCS responses came from 47 students (GAM1) and 51 students (GAM2/PC-AI).
3. Exploratory item-level analysis showed the AI-supported condition stronger on feedback/support and adaptive repetition; conventional gamification higher on interest, clarity, sustained attention, and emotional involvement.
4. More structured or AI-supported gamification does not automatically produce a stronger motivational profile.
5. The paper proposes ARCS as a cognitive-ergonomic lens for assessing motivational Sustainability, usability, and transferability to workplace training design.

Discussion

The paper contributes a methodological framing — treating learner motivation as a form of "cognitive ergonomics" to be measured and designed for, rather than assumed — and delivers a null result that challenges the automatic-association between AI-supported gamification and enhanced Motivation. Its differentiated item-level findings are the actionable core: AI-supported progression excels at instrumental support (feedback, adaptive repetition) while conventional gamification better sustains affective engagement (interest, emotional involvement). For the knowledge base, it connects gamified learning, motivation theory, and instructional design, and pairs naturally with the author's companion NASA-TLX workload study, together illustrating standardized instrument-based evaluation of AI-supported learning environments.

Connected Concepts

• Game-Based Learning
• Motivation
• Higher Education
• Learning Design
• Self-Determination Theory
• Student Experience
• Student Engagement
• Feedback

Connected Articles

• Perceived Workload Across Traditional, Gamified and Artificial Intelligence-Supported Learning Conditions: A NASA-TLX Study in Higher Education — NASA-TLX Workload Study (companion study)
• A systematic review of student engagement research in adaptive learning platforms — Student Engagement and Adaptive Learning
• How motivation and roles influence metacognitive engagement in student-GenAI interaction — Motivation and Metacognitive Roles of GenAI
• Motivation to shape the future of education with Artificial Intelligence: An international comparison between Switzerland and China — Motivation Shaping Future AI Education

Citation

Speranza, M. (2026). Motivational Ergonomics in Gamified and Artificial Intelligence-Supported Learning: An ARCS Study with Implications for Workplace Training. EdArXiv preprint.