Agentic education · Learning science

Actionable companion · Self-regulation & metacognition

Learning with agency: Exploring the role of agentic AI in fostering self-efficacy, autonomy support, and self-learning motivation in higher education

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): Higher education. Verify the participant population and task before transferring the findings.

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

Abstract Introduction Rapid adoption of Artificial Intelligence (AI) in learning has revolutionized learners’ engagement but comprehension of psychological and technological drivers of successful AI-enabled learning remains scarce. This research investigates how students’ perceived agency of AI, usefulness, ease of use, trust, autonomy supporting, and self-efficacy collectively impact students’ self-learning behavior and motivation. Based on Technology Acceptance Model (TAM), Social Cognitive Theory (SCT), and Self-Determination Theory (SDT) theories, our research model predicts an integrated model of motivational and behavioral processes underlying AI adoption in learning settings. Methods We adopted and followed a quantitative research design with a structured questionnaire administered among 280 higher education students in Saudi Arabia. We applied Structural Equation Modeling (SEM) using SmartPLS 4 to analyze data. Results Findings indicate that students’ perceived agency of AI significantly predicts usefulness, ease of use, and autonomy supporting, while ease of use significantly enhances AI-enabled self-efficacy. Self-efficacy and autonomy supporting significantly impact self-learning motivation, driving self-learning behavior positively. But usefulness and trust in AI failed to influence self-efficacy directly, which reveals cultural and contextual settings. Discussion This research adds richness to the fusion of TAM, SCT, and SDT theories in illustrating how AI’s perceived autonomy and usability collectively promote self-directed learning motivation. This research also provides guidelines to educators and system designers to design AI tools that promote learner autonomous settings, usability, and confidence. Future research ought to perform longitudinal and cross-cultural validations to fine-tune theoretically. Keywords: agentic AI, artificial intelligence, autonomy support, higher education, self-efficacy, self-learning motivation

What kind of evidence is this?

Supporting AI learning science · Survey / observational study

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

Data were gathered from teaching staff members and faculties in Saudi Arabian universities using a structured questionnaire in Google Forms. This approach was selected due to its effectiveness in reaching geographically separated respondents and maintaining anonymity and convenience (Bryman, 2016). A non-probability purposive sampling scheme was used, with targets set on respondents who had experience utilizing AI-enabled learning tools (like ChatGPT, AI tutor, or adaptive learning systems) to teach or to undergo professional development. A total of 320 questionnaires were put into distribution, with 280 valid observations being shortlisted for final analysis after data screening. The size of the sample satisfies the prerequisite to apply PLS-SEM analysis since Hair Jr et al. (2021) suggest at least 10 times the highest number of structural paths with an end point on a construct. Demographic characteristics of respondents revealed that 58% were male, and 42% were female, with an average teaching experience of 7 years, and all the respondents had previous exposure to learning settings with AI assistance. Prior to the main questionnaire, a pilot study was carried out with 40 respondents to check the clearness, dependability, and face validity of the instrument. Responses guided minor adjustments in the phrasing of the items. Pilot analysis revealed Cronbach’s alpha ratings exceeding 0.80 in all the constructs, representing high internal consistency (Nunnally and Bernstein, 1994). In dealing with ethical matters, informed consent was undertaken with all the participants with the aspect of voluntary participation, data confidentiality, and anonymity highlighted. No personally identifiable data were gathered. Study protocol was reviewed and cleared by the research ethics committee, and it was in line with institutional and country-based ethical guidelines (Alwakid and Dahri, 2025).

Limitations and uncertainty · source passage

Abstract Introduction Rapid adoption of Artificial Intelligence (AI) in learning has revolutionized learners’ engagement but comprehension of psychological and technological drivers of successful AI-enabled learning remains scarce. This research investigates how students’ perceived agency of AI, usefulness, ease of use, trust, autonomy supporting, and self-efficacy collectively impact students’ self-learning behavior and motivation. Based on Technology Acceptance Model (TAM), Social Cognitive Theory (SCT), and Self-Determination Theory (SDT) theories, our research model predicts an integrated model of motivational and behavioral processes underlying AI adoption in learning settings. Methods We adopted and followed a quantitative research design with a structured questionnaire administered among 280 higher education students in Saudi Arabia. We applied Structural Equation Modeling (SEM) using SmartPLS 4 to analyze data. Results Findings indicate that students’ perceived agency of AI significantly predicts usefulness, ease of use, and autonomy supporting, while ease of use significantly enhances AI-enabled self-efficacy. Self-efficacy and autonomy supporting significantly impact self-learning motivation, driving self-learning behavior positively. But usefulness and trust in AI failed to influence self-efficacy directly, which reveals cultural and contextual settings. Discussion This research adds richness to the fusion of TAM, SCT, and SDT theories in illustrating how AI’s perceived autonomy and usability collectively promote self-directed learning motivation. This research also provides guidelines to educators and system designers to design AI tools that promote learner autonomous settings, usability, and confidence. Future research ought to perform longitudinal and cross-cultural validations to fine-tune theoretically. Keywords: agentic AI, artificial intelligence, autonomy support, higher education, self-efficacy, self-learning motivation

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.

  • 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.

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. 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.
  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. 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

Jehad Alqurni. Exploring the role of agentic AI in fostering self-efficacy, autonomy support, and self-learning motivation in higher education. Frontiers in Artificial Intelligence (2026-01-22). https://doi.org/10.3389/frai.2026.1738774

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.