Agentic education · Learning science

Actionable companion · Feedback & scaffolding

Learning with agency: Exploring an activity-enhanced scaffolding for knowledge structure representing: could the knowledge-activity connection promote students’ online learning experience in AI curriculum?

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.

Read original paperSearch paper agentCC BY 4.0

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

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

Abstract Introduction The effectiveness of online learning is often hindered by fragmented content organization and limited guidance, leading to reduced student engagement and learning outcomes. Methods This study introduces an activity-enhanced scaffolding for knowledge structure representing that combines dynamic knowledge network visualization with adaptive activity pathways to address these challenges. The approach provides students with explicit connections between learning resources and activities while visualizing course knowledge structures and learning progression. We conducted a four-week quasi-experimental study with 60 middle school students to examine the approach’s effectiveness. Results Students were divided into experimental (n = 30) and control (n = 30) groups, with the experimental group using the scaffolded learning environment while studying an artificial intelligence curriculum. Using both quantitative and qualitative methods, we analyzed learning performance through knowledge tests and concept mapping, assessed learning attitudes via questionnaires, and evaluated thinking levels through the Structure of the Observed Learning Outcome (SOLO) taxonomy. Discussion Results revealed that the experimental group showed significantly higher scores in knowledge acquisition and knowledge organization. Moreover, experimental group demonstrated more positive learning attitudes across behavioral, cognitive, and emotional dimensions, and achieved higher levels of thinking, with 73.33% reaching relational or extended abstract levels compared to 21.11% in the control group. These findings suggest that integrating knowledge visualization with structured learning pathways effectively enhances online learning outcomes. The study contributes to both theoretical understanding of scaffolding design and practical guidelines for developing more effective online learning environments. Keywords: adaptive learning, knowledge network, knowledge-activity scaffolding, learning outcome, online learning

What kind of evidence is this?

Intelligent tutoring & adaptive learning · Evidence synthesis

Stage: Secondary 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 study involved 60 students from two parallel classes in Grade 1 at Y Middle School in northern China. Students ranged in age from 15 to 17 years. The experimental group consisted of 30 students (12 girls and 18 boys), while the control group included 30 students (15 girls and 15 boys). All participants possessed prior online learning experience and demonstrated proficiency in computer-based tasks such as concept mapping and online questionnaire completion. We got the informed consent from the students when we did the research in the pre-test questionnaire. All the students agreed to participate in the study.

Limitations and uncertainty · source passage

To address these limitations, we propose an activity-enhanced scaffolding for knowledge structure representing that combines two innovative components: (1) a dynamic knowledge network visualization that reveals relationships between concepts, learning resources, and learner interactions, and (2) an adaptive activity pathway system that provides explicit connections between learning content and activities. This dual-component approach differs from traditional scaffolding by focusing on the organizational structure of knowledge and learning processes rather than just content support. The approach aims to reduce cognitive load (Munshi et al., 2023) while facilitating deeper engagement with course materials.

Design the system before the pilot

Document the learner model, mastery rule, hint sequence and adaptation signal. Compare adaptive routing with the same content in a non-adaptive sequence. Check whether early errors lead to persistent low-expectation pathways.

  • Give feedback on the learner’s reasoning, then fade hints. Record whether the learner can complete the next task without that scaffold.
  • 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. Ask learners to attempt a problem unaided, compare an agent hint, explain a revision and verify the final reasoning against a trusted source. Make the permitted use explicit for each assignment. 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. Measure unaided transfer and delayed retention, calibration of confidence and the ability to identify an incorrect AI explanation. Record whether help-seeking becomes answer copying. 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

Haipeng Wan; Rongping Que; Lingna Cheng; Shan Li; Qi Wang; Jing Liu. Exploring an activity-enhanced scaffolding for knowledge structure representing: could the knowledge-activity connection promote students’ online learning experience in AI curriculum?. Frontiers in Psychology (2026-01-08). https://doi.org/10.3389/fpsyg.2025.1725612

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.