Perceived Workload Across Traditional, Gamified and Artificial Intelligence-Supported Learning Conditions: A NASA-TLX Study in Higher Education

Synthesis: Speranza (2026) compares perceived workload across three conditions in a university experiment — traditional individual study, conventional gamification, and gamification with conditioned progression supported by artificial intelligence functions — using the NASA Task Load Index (NASA-TLX). Welch ANOVAs found no statistically significant differences between the groups on any of the six workload dimensions: mental demand was high across all conditions while physical demand remained low, as expected for a study-based digital task. An exploratory pairwise-weighting analysis revealed two significant differences (mental demand versus perceived performance, and temporal demand versus perceived performance). The author concludes that neither gamification nor AI-supported progression simply increases or reduces workload; rather, cognitive Sustainability depends on how the learning path is designed, paced, explained, and supervised.

Key Findings

1. No statistically significant differences in perceived workload (NASA-TLX) were found across traditional, gamified, and AI-supported learning conditions on any of the six dimensions.
2. Mental demand was high in all conditions; physical demand remained low — consistent with a study-based digital task.
3. Exploratory NASA-TLX pairwise weighting revealed two significant differences involving mental demand versus perceived performance and temporal demand versus perceived performance.
4. Results do not support a simple claim that gamification or AI-supported progression either raises or lowers workload — cognitive sustainability hinges on design, pacing, explanation, and supervision.
5. The study reads its findings alongside NASA-TLX research in professional, industrial, and work-related technology training.

Discussion

The paper contributes a measured, null-result perspective to the often-overenthusiastic literature on gamified and AI-supported learning in higher education. Its value lies in complicating the assumption that adding engagement mechanics or AI-driven progression automatically improves the learning experience — here, workload was not reduced by either condition, and mental demand stayed uniformly high. For the knowledge base, it connects student experience, Motivation, and instructional design literatures, and complements findings on how the quality of technology-mediated environments — not their mere presence — shapes outcomes. It also illustrates the use of standardized measurement instruments (NASA-TLX) to assess the cognitive ergonomics of AI-supported learning.

Connected Concepts

• Game-Based Learning
• Higher Education
• Motivation
• Student Experience
• Learning Design
• Self-Regulated Learning
• Learning Analytics
• cognitive load

Connected Articles

• Motivational Ergonomics in Gamified and Artificial Intelligence-Supported Learning: An ARCS Study with Implications for Workplace Training — ARCS Motivational Ergonomics (companion study)
• Automating Learner Assessment: Benchmarking Machine Learning and Deep Learning Models for EEG-Based Familiarity Prediction — Automating Learner Assessment with EEG
• Not All Students Engage Alike: Multi-Institution Patterns in GenAI Tutor Use — GenAI Tutor Engagement Patterns
• A systematic review of student engagement research in adaptive learning platforms — Student Engagement and Adaptive Learning

Citation

Speranza, M. (2026). Perceived Workload Across Traditional, Gamified and Artificial Intelligence-Supported Learning Conditions: A NASA-TLX Study in Higher Education. EdArXiv preprint.