Actionable companion · Feedback & scaffolding
From AI evidence to responsible practice: Applying ChatGPT to tackle the side effects of personal learning environments from learner and learning perspective: An interview of experts in higher education
AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not findings 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 participants, intervention and comparator before applying this study in a new setting.
Original authors’ abstract, reproduced under the linked source license:
This paper investigates the capacity of ChatGPT, an advanced language model created by OpenAI, to mitigate the side effects encountered by learners in Personal Learning Environments (PLEs) within higher education. A series of semi-structured interviews were conducted with six professors and three Information and Communication Technology (ICT) experts. Employing thematic analysis, the interview data were assessed, revealing that the side effects stemming from the learner and learning perspectives could be primarily categorized into cognitive, non-cognitive, and metacognitive challenges. The findings of the thematic analysis indicate that, from a cognitive standpoint, ChatGPT can generate relevant and trustworthy information, furnish personalized learning resources, and facilitate interdisciplinary learning to fully actualize learners’ potential. Moreover, ChatGPT can aid learners in cultivating non-cognitive skills, including motivation, perseverance, self-regulation, and self-efficacy, as well as metacognitive abilities such as self-determination, self-efficacy, and self-regulation, by providing tailored feedback, fostering creativity, and stimulating critical thinking activities. This study offers valuable insights for integrating artificial intelligence technologies to unleash the full potential of PLEs in higher education.
What kind of evidence is this?
Supporting AI learning science · Qualitative / mixed methods
Stage: Higher education. 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 received approval from the Wenzhou University Review Board, bearing the approval number IORG/IRB #202303. For data collection, online semi-structured interviews were carried out during 20–27 February, 2023, utilizing the Tencent Conference platform. The interviews were conducted in English and lasted between 25 to 30 minutes. Following their participation, each interviewee was sent a letter of appreciation acknowledging their valuable contribution. Throughout the research, the team was deeply conscious of ethical standards and was committed to upholding them. Before proceeding with interviews, written informed consent was secured from every participant via email, reflecting our commitment to respecting their choices. Strict confidentiality was maintained regarding the participants’ information, ensuring their anonymity and privacy. Their data was handled with utmost discretion, and their identities were never mentioned in any resulting publications or reports.
Limitations and uncertainty · source passage
This research reviewed the literature and summarized some of the side effects of PLEs when implemented in the higher education. From the learner and learning perspective, the challenges were categoried into three types, including cognitive, non-cognitive and meta-cognitive. Expert interview was conducted to tackle these challenges. The analysis of expert interviews reveals the potential of ChatGPT in addressing challenges faced by learners using PLEs, particularly in terms of digital literacy and information management. ChatGPT’s real-time support and guidance offer personalized feedback, recommendations, and resources tailored to individual needs and preferences. In terms of non-cognitive skill development, ChatGPT can enhance motivation, perseverance, self-regulation, and self-efficacy in PLE users by providing timely feedback, promoting goal-setting, and encouraging self-reflection. Moreover, it fosters peer-to-peer interaction, building critical social skills and a sense of belonging among learners. Regarding metacognitive skills, ChatGPT assists learners in developing self-determination, self-efficacy, and self-regulation by offering personalized feedback, goal monitoring, and guidance in managing Web 2.0 tool. Furthermore, it supports critical thinking, information evaluation, and effective digital media consumption.
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.
- Use plan–attempt–check–reflect prompts and compare confidence with actual performance. Keep learner choice and reflection visible.
- 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
- 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. 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.
- 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. 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
XiaoShu Xu; XiBing Wang; YunFeng Zhang; Rong Zheng. Applying ChatGPT to tackle the side effects of personal learning environments from learner and learning perspective: An interview of experts in higher education. PLOS ONE (2024-01-03). https://doi.org/10.1371/journal.pone.0295646
Companion prepared 2026-11-01. 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.