Agentic education · Learning science

Actionable companion · Motivation & engagement

Learning with agency: Quantum psychology approach on enjoyment as mediator in relationship between L2 flow and engagement

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

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

Introduction Quantum psychology offers scholars a novel lens, to be preferred when Chaos/Complexity falls short of understanding the missing puzzle pieces in the complex interplay between cognitive and affective factors in second language acquisition (SLA). Adopting a quantum psychology approach, this study conceptualizes learner psychology as probabilistic and non-linear, proposing that flow, enjoyment, and engagement are dynamically entangled, akin to quantum principles of superposition and entanglement. Methods To test this, the study investigates the mediating role of enjoyment in the relationship between psychological flow and academic engagement among 162 high school students in artificial intelligence (AI)-assisted English as a Foreign Language (EFL) speaking and writing classes. Employing a quantitative, cross-sectional design, data were collected using a validated Psychological Flow Scale, Academic Engagement Scale, and Foreign Language Enjoyment Scale. Results Path analysis has revealed that psychological flow does not directly influence academic engagement; however, flow significantly predicts enjoyment, which in turn positively affects engagement. The indirect effect of flow on engagement through enjoyment was significant and indicated full mediation. Discussion These findings challenge linear models of flow and engagement in SLA. The results, interpreted through a quantum perspective, emphasize the importance of fostering enjoyable learning experiences to enhance engagement, particularly in technology-enhanced contexts like AI-assisted classrooms. Pedagogical implications include designing interactive, enjoyable, and optimally challenging tasks to promote flow and enjoyment to sustain learner engagement. Future studies should investigate more mediators and use longitudinal designs to clarify these evolving relationships.

What kind of evidence is this?

Supporting AI learning science · Survey / observational study

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

This study employed a quantitative, cross-sectional research design to investigate the relationships among psychological flow, enjoyment, and academic engagement in English language classes. This design was chosen over longitudinal or experimental alternatives because it aligned with the objectives of our study. It allowed us to efficiently examine the mediating role of enjoyment in the flow and engagement relationship and to capture their probabilistic interplay at a single point in time using validated scales and path analysis. This design was particularly suitable for mediation analysis when the goal was to test whether a variable, such as enjoyment, mediated the relationship between two others, such as flow and engagement. Moreover, cross-sectional studies though limited in establishing causality offer efficiency in data collection and the ability to examine a probabilistic interplay of constructs simultaneously (Bryman, 2016).

Limitations and uncertainty · source passage

While this study provides valuable insights into Flow, Enjoyment, and Engagement, several limitations should be acknowledged. First, the explained variance in Engagement was relatively low (R2 = 0.094), suggesting that other factors may also contribute to engagement. Future research should explore additional mediators and moderators to provide a more comprehensive understanding of engagement in AI-assisted learning environments. The cross-sectional design employed in this study captured a precise snapshot of the probabilistic interplay among flow, enjoyment, and engagement at a single point, though it does not track their dynamic evolution over time, which could be explored in future longitudinal studies. Our study was limited to a specific Turkish educational context, the study can be replicated by further investigating enjoyment as a potential mediator when the results show no direct link between flow and engagement despite expectations. Future studies in other context could help more generalizability of the findings. Our study employed established self-report instruments which is widely accepted in social sciences, but these measures might be somewhat susceptible to biases such as social desirability or recall error, yet the high internal consistency values (α = 0.78–0.92) lend strong support to the reliability of our findings. While our investigation theoretically operationalized quantum psychology with self-reported data, future research must work toward designing and employing time-sensitive tools such as ecological momentary assessments or sensor-based techniques to achieve more robust results.

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.

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

Ferdi Çelik. Quantum psychology approach on enjoyment as mediator in relationship between L2 flow and engagement. Frontiers in Psychology (2025-07-21). https://doi.org/10.3389/fpsyg.2025.1593973

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.