AI Anxiety and Confidence Shape How Students Engage in Higher Education

Artificial intelligence has swept into higher education faster than most pedagogical frameworks can absorb it. Adaptive learning platforms, intelligent tutoring systems, automated grading, plagiarism detection, and generative writing tools now share the classroom with human instructors, and students are no longer merely learning new content. They are negotiating a relationship with a machine that appears to think. A new narrative review published in Discover Education examines how the emotional fallout of that negotiation, particularly AI anxiety and AI confidence, shapes student engagement, the well-established predictor of academic success that encompasses behavioural participation, emotional investment, and cognitive involvement in learning.

The review, conducted by Shakiba Moradi and Pete Evans, synthesized literature published between 2015 and 2026, identified through structured searches of PubMed, Scopus, Web of Science, and Google Scholar. Rather than estimating pooled intervention effects, the authors organized findings thematically across four domains: the nature and sources of AI anxiety, the development of AI confidence and self-efficacy, the emotional and motivational mechanisms associated with engagement, and the individual, disciplinary, cultural, and institutional conditions that alter these relationships. Self-Determination Theory, Bandura’s Self-Efficacy Theory, and research on emotion-cognition interactions provided the principal interpretive lenses.

Across the reviewed literature, AI anxiety was generally associated with avoidance, reduced perceived control, cognitive burden, and reluctance to experiment. Students who perceive AI as a threat to their intellectual autonomy or career relevance may experience lower participation and cognitive overload. Confidence and self-efficacy, by contrast, were linked to persistence, exploration, and more active use of AI-supported learning tools. Yet the review cautions that these relationships are neither uniform nor necessarily causal, and that much of the current evidence relies on cross-sectional, self-reported data, making it difficult to determine whether anxiety causes disengagement, whether disengagement intensifies anxiety, or whether both are shaped by unmeasured contextual factors.

At a conceptual level, AI anxiety resembles broader technostress but carries unique features. Traditional technostress stems from overload or complexity, whereas AI anxiety is entangled with existential and epistemic concerns: the fear of being replaced, misjudged, or outperformed by intelligent systems. It also encompasses uncertainty about ethical use, academic integrity, and data privacy. The review identifies three intertwined dimensions: cognitive, reflecting confusion and overload; emotional, manifesting as fear, stress, or avoidance; and existential, tied to meaning and identity. Cognitive psychology offers further insight, noting that anxiety thrives where predictability is low and perceived demands exceed perceived resources, conditions that opaque neural networks and probabilistic models resist intuitive comprehension.

Confidence operates through the opposite loop. When students experience mastery or even partial success, they interpret that success as evidence of capability, fueling motivation and deeper exploration. Each subsequent encounter reinforces efficacy beliefs, creating a positive spiral of growth. Bandura identified four principal sources of efficacy beliefs: mastery experiences, vicarious experiences, social persuasion, and physiological states. Early exposure to low-stakes experimentation is crucial, as is constructive feedback that highlights human judgment alongside AI suggestions. Confidence is also socially contagious; observing peers who use AI effectively normalizes engagement and reduces intimidation. Importantly, the review warns that overconfidence can lead to uncritical trust in machine output, so the optimal stance is calibrated confidence, a balanced awareness of both AI’s utility and its limitations.

Self-Determination Theory offers a complementary framework. The theory emphasizes three universal psychological needs: autonomy, competence, and relatedness. AI technologies, depending on their design, can either enhance or erode each. Adaptive platforms can personalize pace and difficulty, allowing students to pursue mastery in self-directed ways, yet opaque algorithms that make decisions invisible to the user diminish ownership. Competence involves not only task success but emotional appraisal; feedback devoid of empathy can reinforce the impression that the learner is being judged by an unfeeling machine. Relatedness may suffer if students perceive AI as replacing human contact, though partially automating routine feedback can free educators to focus on mentorship and dialogue.

The review also maps contextual factors that shape these relationships. Prior technological experience and AI literacy are among the most consistent moderators; students with greater digital competence tend to approach AI with curiosity rather than fear. Gender and identity dimensions, disciplinary culture, cultural narratives and media discourses, and institutional climate all influence how AI is interpreted, as a learning resource, an evaluative threat, or a challenge to human agency. Universities that present AI integration as a top-down mandate may inadvertently evoke resistance, while those that frame it as co-creation foster ownership. Assessment policies that penalize improper AI use without clear guidance can heighten anxiety by casting technology as a disciplinary threat rather than a learning tool.

Drawing on recurring patterns across the literature, the authors propose a provisional five-stage framework of emotional adaptation: curiosity, anxiety, experimentation, confidence, and flourishing. The framework is explicitly intended as a heuristic rather than a universal or empirically validated sequence; students may enter at different points, move between stages, remain ambivalent, or experience confidence and anxiety simultaneously. Curiosity marks the initial encounter, when novelty stimulates exploration but understanding remains limited. Anxiety intensifies as complexity becomes apparent and questions arise about correctness, judgment, and obsolescence. Experimentation follows once anxiety stabilizes, as risk-taking replaces avoidance and mistakes are reframed as data rather than failure. Confidence solidifies through accumulated mastery experiences, and flourishing represents the integration of cognitive mastery, emotional stability, and ethical awareness, a dynamic equilibrium rather than a permanent state.

For educational practice, the review supports a layered response rather than a single intervention. At the instructional level, students should receive scaffolded opportunities to use AI in low-stakes settings, compare AI outputs with human reasoning, identify errors and biases, and reflect on their own emotional and cognitive responses. At the institutional level, AI literacy should include not only operational skills but also understanding of uncertainty, hallucination, bias, privacy, authorship, and appropriate verification. Clear policies are needed to distinguish acceptable educational use from misconduct, and institutions should explain when AI is being used in assessment, advising, monitoring, or feedback and how student data are collected and interpreted. Human mediation remains essential; faculty guidance, peer support, mentorship, and psychologically safe discussion can help students interpret technological difficulty as part of learning rather than evidence of personal inadequacy.

Ultimately, the review argues that flourishing engagement should not be equated with uncritical enthusiasm for artificial intelligence. It is better understood as the capacity to engage with AI confidently, reflectively, ethically, and without surrendering human judgment. Educational success in the age of AI will depend not only on technological capability but also on whether institutions create conditions in which students can question, evaluate, and use these systems with informed agency. The authors call for longitudinal, experimental, qualitative, and mixed-method research to test the proposed mechanisms and identify which interventions most effectively support students who experience AI-related anxiety, noting that the direction and temporal ordering of the proposed relationships remain theoretically inferred rather than conclusively established.

Subject of Research: Educational Psychology

Article Title: How AI anxiety and confidence shape student engagement in higher education

Article References: Moradi, S., & Evans, P. (2026). How AI anxiety and confidence shape student engagement in higher education. Discover Education, 5(1), Article 857. https://doi.org/10.1007/s44217-026-02078-5

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02078-5

Keywords: AI anxiety, student engagement, self-efficacy, higher education, emotion regulation, AI literacy, anxiety, confidence, shape, student, engagement, higher