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Self-Efficacy

created 2026-08-13 · updated 2026-09-23

Self-efficacy — a learner's belief in their capability to successfully perform a task or achieve a goal. Drawing on social cognitive theory (Bandura), self-efficacy shapes motivation, effort, persistence, and learning engagement. In AI in education, self-efficacy matters in two ways: AI tools can build learners' confidence and autonomy (e.g., by providing Feedback and Scaffolding), and learners' AI self-efficacy — their confidence in using AI technologies — influences how effectively they engage with AI, including how AI-related knowledge translates into career-relevant readiness.

Questions to Consider

  • Self-efficacy is a belief about your capability — distinct from actual competence. Have you ever been highly capable at something yet doubted yourself, or confidently wrong about something you couldn't do? What does that gap between belief and ability tell you about why self-efficacy matters?
  • AI self-efficacy (confidence working with AI) is a separate construct from AI literacy, and research finds literacy translates into readiness only when learners also have confidence. If someone knows about AI but doesn't believe they can use it, what happens to that knowledge — and what does that imply for training?
  • Research found that using AI to support understanding was fully mediated by academic self-efficacy in its link to performance, while shortcut use predicted worse outcomes partly independent of self-efficacy. Why would the same tool build confidence when used one way and fail to when used another?
  • The page distinguishes self-efficacy from the everyday word 'confidence.' Before you read, how are they different, and why would a researcher insist on the distinction rather than treating them as the same thing?
  • Robotics and embodied learning build confidence by grounding tasks in observable outcomes, and feedback can build learner self-efficacy. Think of a task where you gained real confidence only after seeing a concrete result. What does that say about what kinds of AI learning experiences are most likely to build — rather than merely report — self-efficacy?
  • Teacher self-efficacy affects adoption and integration of AI. If a teacher doesn't believe they can use AI effectively, does any amount of AI literacy fix it? What would build a teacher's confidence, and how is that different from giving them more information?

Introduction

Self-efficacy is distinct from actual competence: it is a belief about capability that drives behavior. It is closely related to — and often used interchangeably with — the everyday notion of confidence in one's abilities. Self-efficacy connects closely to Motivation, Self-Regulated Learning, and Student Experience. In the AI context, AI self-efficacy (confidence in working with AI) is a distinct construct from AI literacy, and research shows it plays a crucial role in whether learners actually activate and apply AI-related knowledge.

How self-efficacy appears in the knowledge base's research

  • Declines under GenAI-plus-XR studio work: in a 27-student architectural design studio, teams using a GenAI and multi-user XR pipeline showed larger relative pre–post declines in design self-efficacy confidence (β = −1.675, p = 0.021) and outcome expectancy (β = −2.088, p = 0.002) than teams continuing the normal workflow, with no significant difference in blinded panel ratings of their presentations (Xiao et al., 2026). Tool-rich environments can depress efficacy beliefs even when the work itself is judged equivalent.
  • AI self-efficacy and career readiness: Research on AI readiness shows that AI self-efficacy moderates the relationship between AI literacy and AI readiness: literacy translates into readiness only when learners have confidence in using AI, and self-efficacy directly predicts career adaptability.
  • Trust as the pivot between literacy and confidence: Hu (2026) surveyed 450 university students who already use ChatGPT and found an ordered chain rather than two parallel correlates: AI literacy related to Trust in the tool (beta = 0.50), trust to academic self-efficacy (0.48), and self-efficacy to continued use, with the serial indirect effect significant (0.07, 95% CI [0.04, 0.10]). AI anxiety weakened the literacy to trust link (interaction beta = -0.25, simple slopes falling from 0.76 to 0.25 across the anxiety range), so the same knowledge bought less confidence, and reached less use, among more anxious students. Confidence is thus a downstream link in the chain rather than a starting point.
  • Profiles of academic self-efficacy and who reaches for AI: Suriá-Martínez et al. (2026) ran a latent profile analysis of academic self-efficacy among 102 university students with motor disabilities in Spain and found three profiles (low 29.4%, moderate 41.2%, high 29.4%) whose reported AI use rose stepwise with profile level (means 2.41, 3.56, 4.68; F(2, 99) = 27.84, p < .001, eta squared = .36), with Excellence, the planning and goal-setting dimension, most strongly associated with AI use (beta = .47). The authors read this as an equity problem: if confidence tracks with uptake, students with lower self-efficacy may be the least likely to reach for AI support that could reduce access barriers, so support for academic self-efficacy belongs inside inclusion frameworks.
  • AI use patterns and self-efficacy: Stamatoulis et al. (2026) found that using GenAI to support understanding (evaluative integration) was fully mediated by academic self-efficacy in its association with performance — understanding-oriented AI use builds confidence — whereas shortcut use (low-verification uptake) predicted worse outcomes partly independently of self-efficacy. Self-efficacy is thus both a pathway through which productive AI use helps and a factor that shortcut use may fail to build.
  • Robotics and hands-on learning: REMind's robot-mediated role-play built children's self-efficacy in anti-bullying intervention; robotics and embodied learning generally build confidence by grounding tasks in observable outcomes.
  • Creative self-efficacy in children: Niu et al. (2026) report, in a systematic scoping review of 22 primary studies of generative AI with children aged 6 to 15, that gains in creative self-efficacy cluster with divergent thinking and narrative creativity, mostly through text-to-image tools that lower the barrier between idea and artifact. They stop short of claiming efficacy: the designs are heterogeneous, the review performed no critical appraisal, and the corpus is nearly silent on disability, low-connectivity and underserved learners, so creative confidence is an outcome reported to rise rather than one the field has confirmed.
  • Teacher self-efficacy: Teacher AI competency research examines how professional development builds teachers' confidence in using AI, which affects adoption and integration. Ismael, Luo & Li (2026) add quasi-experimental evidence that an AI-supported e-mentoring model raises EFL pre-service teachers' self-efficacy and emotional intelligence during the practicum: the experimental group gained substantially more than controls on both measures, with a large between-group effect and gains across all self-efficacy subdomains — and the authors attribute them to mentoring AI-mediated within structured reflective cycles rather than to the AI alone.
  • Feedback and confidence: AI feedback and tutoring can build learner self-efficacy by providing actionable, supportive feedback.
  • Empowerment in AI Problem Solving: Zhu and Kong (2026) find that students' empowerment in using AI for problem solving mediates the relationship between perceived project-based learning and satisfaction with an AI literacy course. In their SEM analysis of 1,027 students, PBL fostered conditions that empowered students to use AI for problem solving, which in turn drove course satisfaction — evidence that building students' confidence and capability with AI is a key mechanism of effective AI literacy education.

Self-efficacy connects to Motivation, Self-Regulated Learning, Student Experience, AI Literacy, Learner Agency, and Robots in Education. Building self-efficacy is a key mechanism through which AI supports engagement and learning. Self-efficacy is measured almost entirely by self-report, so its associations with observed behavior deserve the usual caution.

  • AIGC self-efficacy as the pivot between tool and learning. Liang et al. (2026) find that perceived affordances of AI-generated content raise AIGC self-efficacy (beta = 0.583), which then mediates the paths to learning motivation (indirect effect 0.329) and to self-regulated learning (0.145), with the serial path affordance to self-efficacy to motivation to self-AI Regulation in Education also significant (0.173). The contrast that makes the finding useful is that the quality of AI Assessment feedback did not predict self-efficacy (beta = 0.131, n.s.) even though it strongly predicted satisfaction — confidence with the tool is built by directing it, not by receiving good output from it.

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Metadata

Intended audienceLearners
Learning and instructionMotivation, Self-Efficacy, Self-Regulated Learning
Assessment and measurementAssessment
Foundations of AI in educationAI Literacy
SourceHistoryEditChat

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