Actionable companion · Motivation & engagement
Learning with agency: A Humane Social Learning-Informed Metaverse: Cultivating Positive Technology Experiences in Digital Learning Environments
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:
Abstract Online environments, such as metaverses, provide distinct social environments for people to engage in complex, cognitive, and multidirectional learning and meaning-making experiences. These engaging and influential environments highlight important factors associated with the Social Learning Theory (a process through which external settings influence behavior in specific environments). According to this theory, environments provide a space for youth to engage in reciprocal interactions of interpersonal, behavioral, and environmental cues. Online environments designed by social media companies have been scrutinized, given their dependence on algorithms (artificial intelligence systems). Research has revealed the effects of systems that use machine learning to subversively maintain engagement on their platforms for as long as possible. Given the constant changes in socializing environments, younger generational cohorts need to be adequately prepared for systems that determine what type of content they are exposed to, and shape the timing, frequency, and agentic influencers they engage with. Therefore, this article proposes a necessity to expand our understanding about social learning and current technology design principles. This article demonstrates the need for a paradigm shift toward exploring an innovative construct referred to as the digital learning environment. We examine existing issues in the design of digital spaces, provide a positive developmental psychology framework that informs further research, and propose solutions for researchers, educators, policymakers, and caregivers as they navigate healthy technology use and predominant mental health issues in the 21st century. Keywords: technology, metaverse, social learning theory, social media, artificial intelligence, adolescence, social media, environment, positive psychology, learning, engagement, digital learning
What kind of evidence is this?
Supporting AI learning science · Design requires full-text appraisal
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.
Limitations and uncertainty · source passage
We can therefore posit that for people who experience increased positive technology experiences at greater frequency than negative experiences, this might result reciprocally in increased positive emotional experiences of connecting to self and others. It would be interesting for future research to study whether positive technology experiences correlate with positive emotional experiences within the context of interpersonal relationships or community settings within nondigital environments, thus contributing to our overall understanding of the affective impact of digital learning environments.
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
- 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
Andrew Villamil; Sará King. A Humane Social Learning-Informed Metaverse: Cultivating Positive Technology Experiences in Digital Learning Environments. Cyberpsychology, Behavior and Social Networking (2024-01-01). https://doi.org/10.1089/cyber.2023.0001
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.