Agentic education · Learning science

Actionable companion · Motivation & engagement

Learning with agency: The Effectiveness of Teacher Support for Students’ Learning of Artificial Intelligence Popular Science Activities

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.

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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): Primary school. Verify the participant population and task before transferring the findings.

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

The burgeoning of new technologies is increasingly affecting people’s lives. One new technology that is heatedly discussed is artificial intelligence (AI) in education. To allow students to understand the impact of emerging technologies on people’s future lives from a young age, some popular science activities are being progressively introduced into elementary school curricula. Popular science activities are informal education programs and practices of universal education. However, two issues need to be discussed in the implementation of these activities. First, because these informal curricula are usually short in duration, the question of whether they only serve to generate motivation or actually enhance learning outcomes requires examination. Second, the role of teacher support in popular science activities and its impact on students’ learning results need to be further investigated. To this end, this study aims to explore the effectiveness of popular AI science activities in informal curricula on students’ AI achievement and the interrelationship between students’ learning outcomes in popular AI science activities with and without teacher support. A 6-h-long AI popular science activity was conducted with 22 fifth- and sixth-grade students in elementary school. This study was conducted using a one-group pretest and posttest design, and the data collection tools included AI achievement pre- and posttests and an artifact scoring rubric. The results showed that with regard to learning outcomes, popular science activities were helpful for cognitive enhancement of AI concepts, but more time was needed for skills to improve. Additionally, this study found that students’ learning performance was different with and without teacher support. Activities with teacher support can enhance students’ learning outcomes, but students become accustomed to relying on their teachers. In contrast, activities without teacher support seem to be more effective in fostering students’ independent computational thinking and problem-solving abilities.

What kind of evidence is this?

Supporting AI learning science · Comparative / pre-post study reported

Stage: Primary school. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

The participants of this study were elementary school students in grades 5–6 who applied to participate voluntarily. All of the participants had basic knowledge about a visual programming language, for example, Scratch. A total of 22 students, including 16 boys and six girls, participated in the program; they were divided into 11 random pairs during the activity.

Limitations and uncertainty · source passage

The results of this study show that regarding the first research question, AI popular science activities are helpful in terms of cognitive enhancement, while more time may be needed for skill enhancement. In other words, although popular science activities are conducted in a short period of time, they can enhance students’ learning results with an appropriate curriculum design. For the second research question, AI science education activities with and without teacher support have different learning outcomes. AI science education activities with teacher support lead to higher scores on students’ work, that is, the quality of work is better. In the case of AI science education activities without teacher support, although the quality of students’ work is not as good, students can rely on their own computational thinking skills to complete the tasks and perform better in creativity and problem-solving skills, which is another indicator of learning effectiveness. Teacher support is similar to teachers building scaffolds in students’ zone of proximal development (ZPD; Xi and Lantolf, 2021), but it is worth studying how teachers can build a scaffold to avoid making students overly dependent on teachers for learning. Therefore, future research could investigate the effects of different levels of teacher support on students’ AI knowledge and higher-level cognitive abilities to provide a reference for curriculum design.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

  • Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.
  • Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

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. Pilot one teacher-led unit with short guided turns. The tutor asks for an explanation and offers a graduated hint before any worked solution. Use concrete examples and an equivalent task without AI. 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. Use an age-appropriate independent post-task and a delayed parallel task. Score explanation quality and transfer, not the number of agent messages or completion rate alone. 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

Sheng-Yi Wu; Kuay-Keng Yang. The Effectiveness of Teacher Support for Students’ Learning of Artificial Intelligence Popular Science Activities. Frontiers in Psychology (2022-06-03). https://doi.org/10.3389/fpsyg.2022.868623

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.