Actionable companion · Feedback & scaffolding
Learning with agency: The adaptive ecological trap: a grounded theory study of adolescent AI dependency
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): Secondary school. Verify the participant population and task before transferring the findings.
Original authors’ abstract, reproduced under the linked source license:
Introduction This study explores the formation mechanisms of adolescent dependency on artificial intelligence and proposes a theoretical model of the “adaptive ecological trap” to explain its dynamic evolutionary process. Methods Employing a grounded theory approach, in-depth interviews were conducted with 38 secondary school students who frequently used generative AI, and the data were analyzed through three-level coding. Results The results indicated that the formation of adolescent AI dependency is a systemic process that originates from gaps in real-world developmental needs and initiates compensatory use under the combined influence of technological allure and individual-environmental vulnerabilities. According to participants' accounts, this process may be perceived to erode key developmental capacities and potentially contribute to a self-reinforcing cycle. The model delineates a four-stage evolutionary pathway from adaptive use to increasingly impairing dependency: trap laying, triggering, tightening, and locking. This study emphasizes that AI dependency is not merely a behavioral addiction but an outcome of dysregulation within the individual-technology-environment ecosystem, characterized by dynamism, interactivity, and self-reinforcing properties. Discussion The findings call for collaborative interventions involving families, schools, technology platforms, and social policies. Furthermore, measures such as filling real-world developmental gaps, enhancing individual and environmental resilience, and disrupting negative feedback loops are crucial to assist adolescents in achieving healthy, autonomous development in the digital era.
What kind of evidence is this?
Supporting AI learning science · Qualitative / mixed methods
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
Stage 1—Teacher nomination (initial screening). Homeroom teachers from participating middle schools were provided with a one-page behavioral anchor guide (see Supplementary Material S1) listing observable signs consistent with the above conceptualization. Examples included: “the student frequently uses generative AI to complete assignments that peers finish independently without AI”; “the student shows noticeable anxiety, frustration, or protest when advised to limit AI use”; “the student prefers conversing with AI chatbots over face-to-face social activities after school”. Teachers were instructed to nominate students who had exhibited at least two of these indicators during the preceding two months. This step served to identify a pool of potentially relevant cases and was not intended as a clinical diagnosis.
Limitations and uncertainty · source passage
The research trajectory on technology dependency evolved from an initial medical framework of behavioral addiction, focusing on clinical features, such as uncontrolled use and withdrawal symptoms, to theories emphasizing psychosocial compensation (Young, 1998). Owing to the advent of AI possessing highly realistic social capabilities, the explanatory power of purely behaviorist approaches has limitations. The Compensatory Internet Use Model, proposed by Kardefelt-Winther (2014), marked a significant theoretical shift, suggesting that excessive online behavior may not stem from technology's addictive nature but rather from attempts to compensate for unmet psychosocial needs in life. Considering generative AI's powerful appeal among adolescents through this lens, its roots lie in the technology's ability to provide both cognitive and emotional compensation. Regarding cognitive function compensation, Ortega-Ochoa et al. (2024) identified 962 records in their initial search, but after screening, only four studies were included in their mini-review. Within these, they found that generative AI tools may enhance student classroom participation and learning interest, although the limited number of included studies precludes strong conclusions about statistical significance. Pergantis et al. (2025) systematically reviewed AI's potential and current state in the cognitive domain, suggesting positive prospects for enhancing cognitive flexibility, attention, working memory, problem-solving, and metacognition. However, concerns exist that long-term reliance on generative AI may weaken the brain's capacity for deep information processing and long-term memory consolidation, impairing metacognitive abilities (Deckker and Sumanasekara, 2025). However, a more profound impact might be hindering the development of adolescents' independent problem-solving and analytical reasoning skills, leading to a phenomenon termed “intellectual deskilling” (Machidon, 2025).
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.
- Give feedback on the learner’s reasoning, then fade hints. Record whether the learner can complete the next task without that scaffold.
A 90-day district pilot
- 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.
- 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.
- 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.
- 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
Gaochang Gong; Guoqiang Li. The adaptive ecological trap: a grounded theory study of adolescent AI dependency. Frontiers in Psychology (2026-06-18). https://doi.org/10.3389/fpsyg.2026.1839672
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.