Actionable companion · Assessment & reasoning

Learning with agency: Learning Gain and User Experience of AI Avatar–Based and Human-Presented Explainer Videos: Prospective Randomized Crossover Feasibility Study

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

Background Explainer videos are widely used in higher education. With the increasing availability of artificial intelligence (AI)–generated avatars, it remains unclear whether the presentation format—human presenter vs AI avatar—affects learning outcomes and user experience, especially in technologically complex fields. Objective This study aimed to assess the feasibility of a randomized crossover design to investigate learning gain and user experience associated with content-identical explainer videos delivered by either an AI-generated avatar or a human presenter. Exploratory analyses examined the potential differences between the presentation formats. Methods An observer-blinded, prospective randomized crossover feasibility study was conducted with 13 undergraduate engineering students. Participants viewed 2 content-identical explainer videos on fuel cell technology presented by either an AI-generated avatar or a human presenter in a randomized sequence. Learning gains were recorded using a 7-item knowledge test administered at baseline and after the first and second video presentations. User experience was assessed after each video by using the AttrakDiff2 questionnaire. Because there was no washout period and the instructional material was identical in both videos, the second learning phase was vulnerable to carryover and test-retest effects. Consequently, analyses of learning outcomes focused on the initial phase, whereas user experience was examined through pooled comparisons across both conditions. Results Both presentation formats were associated with substantial short-term learning gains. The difference in the learning gain between the AI avatar and human presenter videos was not statistically significant (median newly correct items 5, IQR 3-5.5 vs 4.5, IQR 2.5-5; P=.51; Z=0.66; r=0.183). In contrast, user experience ratings were consistently higher for the human-presented video across all AttrakDiff2 dimensions, with small to medium effect sizes. The AI avatar presentation was generally perceived as neutral. Conclusions This study shows that investigating AI-based explainer videos vs those using a human presenter in classroom settings is feasible and highlights methodological challenges, particularly those related to crossover designs involving content-identical materials. In this small exploratory sample, no significant differences in short-term learning gains were detected between different presentation formats. Nonetheless, participants clearly preferred human presenters in terms of user experience. These results should not be seen as proof of equivalence but rather as a foundation for future research with larger sample sizes, improved study designs, and more sensitive outcome measures.

A 90-day district pilot

1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
2. Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
4. Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Mike Reichert; Thorsten Jungmann; Ute von Jan; Urs-Vito Albrecht. Learning Gain and User Experience of AI Avatar–Based and Human-Presented Explainer Videos: Prospective Randomized Crossover Feasibility Study. JMIR Formative Research (2026-06-16). https://doi.org/10.2196/90037

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.