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Learner Identity

created 2026-08-27 · updated 2026-10-01

Learner identity — the evolving sense of who one is (and who one is becoming) as a learner, encompassing disciplinary, professional, creative, and academic identities. In AI in education, generative AI presses on learner identity in two directions at once: it can support identity formation (Scaffolding disciplinary belonging and confidence) while also threatening it (undermining perceived authorship, competence, and authentic learning). Understanding learner identity is central to designing AI that affirms rather than erodes learners' sense of self.

Questions to Consider

  • Think of a time you felt a skill or piece of work was truly 'yours' versus merely done through you. What made the difference — authorship, recognition, competence? How might a tool that produces the work for you reshape that feeling?
  • A common assumption is that identity is a fixed trait a learner either has or lacks. What would change in how you design learning if you treated learner identity instead as something continuously built through participation, recognition, and authorship?
  • The page describes a 'competence paradox' among art and design students: AI tools feel easy and useful, yet their use threatens the creative identity students derive from manual craft. Have you ever felt your own competence or identity challenged by an easy tool? What was the tension?
  • Students sometimes hide or feel shame about their AI use, which fragments their academic identity and honest engagement. When does the pressure to appear a certain kind of learner push people to conceal how they actually learn, and what would make disclosure feel safe?
  • AI can scaffold identity formation as well as threaten it. If you were designing an AI learning companion, what specific features would protect a learner's sense of authorship and ownership while still offering support?

Introduction

Learner identity concerns who a learner understands themselves to be, and who they are becoming, within a domain — a motivational and developmental construct distinct from ability beliefs such as Self-Efficacy, which answer the narrower question can I do this? Identity forms through participation, recognition and authorship: seeing oneself reflected in a field and having that self-view validated by others. AI reshapes all three conditions, because it changes who does the work, what counts as one's own contribution, and whether a learner is recognized as the author of their own learning (Learner Agency, Metacognition).

Why identity matters for AI in education

Identity is a motivational and developmental construct distinct from (but connected to) related abilities and beliefs. Where Self-Efficacy concerns can I do this?, identity concerns who am I — and who am I becoming? It is built through participation, recognition, and authorship — through seeing oneself reflected in a domain and having that self-view validated. AI reshapes the conditions under which identity forms because it changes who does the work, what counts as one's own contribution, and whether a learner feels recognized as the author of their learning. This makes identity a first-order design concern rather than a peripheral "soft" factor.

  • Authorship and competence under threat. When AI produces text, images, or code, learners may question whether the result is truly "theirs" — a challenge to the authorship dimension of identity. Liu et al. (2026) document a competence paradox in art and design students using text-to-image GenAI: the tools feel easy and useful, yet their use simultaneously threatens the creative identity students derive from manual craft and authorship, producing a genuine tension between ease and self-worth.

A distinct authorship condition, not only a threat to authorship: when self-formative dialogue is habitually delegated to a conversational model, the authors argue the integrative work of authoring is delegated rather than undeveloped or absent — a self densely voiced yet un-authored, which they say existing constructs have no slot for (Du et al. (2026)).

  • Shame and hidden use. Lin et al. show that computing students experience shame and guilt around AI use, which function as social regulators driving hiding and selective disclosure — behaviors that can fragment academic identity and undermine honest engagement with learning.
  • Identity as something AI can scaffold. AI need not only threaten identity. Benedetti (2026) argue that accountable, relationally-oriented pedagogical accompaniment can support learners' STEM identity development by providing transparent, bounded support that leaves room for the learner to own their trajectory.

Identity in the knowledge base's research

  • Creative identity: the T2I competence paradox captures how ease-of-use can undermine the craft-based identity of art and design students.

  • Felt authorship can decouple from actual originality. Design students building websites with AI rated their authorship highly (67/100, satisfaction 6.4/7, perceived quality 6.1/7), yet those ratings tracked nothing measurable: their designs were more homogeneous than without AI, and felt authorship correlated with measured originality at r ≤ +0.13 (Boussioux et al., 2026).

  • Professional identity: multiple studies treat AI's impact on professional identity — for example, Lodge et al. (2026) argue that graduates need adaptive capabilities (AI Literacy, Distributed Cognition, Metacognition) precisely so they can sustain a viable professional identity in an AI-integrated future, rather than being defined by — or defined out by — their tools. GenAI judgments can split by temporal context as well as authorship: interviewees judged AI use inappropriate for learning yet legitimate or expected in future professional work — "As a student I must learn, as a professional I must produce" (Sun et al. (2026)).

  • Post-human and hybrid identity: Elsayed (2026) theorize the post-human learner, whose cognition is genuinely hybrid and distributed across biological and artificial systems — a reframing of identity formation itself in the age of cognitive AI.

  • Student and academic identity: authentic assessment research connects to identity because Assessment tasks that call for authentic, personal performance help students see themselves as capable practitioners; history-education research shows how paternalistic AI use can shape how students construct their identity as disciplinary inquirers.

  • How students frame the tool shapes their professional becoming. Students who cast GenAI solely as a socio-emotional partner produced less differentiated reflections on their professional learning than those who also framed it as a technique, a learning material, or a structure (Bohmer et al., 2026).

  • Guarded adoption: selective AI use as identity protection. Zagami (2026) surveyed 484 students at one Australian university and found that higher-achieving students reported lower active generative AI engagement, lower positive affect toward AI, lower perceived learning impact and lower AI-related disengagement, with the strongest associations at rho = -0.395 for perceived learning impact and rho = -0.359 for positive affect. Their open-ended responses described use that was selective (clarification, summarization, workflow support), verified, and held subordinate to their own judgment, and item-level results showed greater agreement that reliance on AI hinders Critical Thinking and independent problem solving. The authors read this as identity work: for students whose sense of themselves as successful learners rests on their own effort and judgment, bounding AI use defends the epistemic agency the identity depends on — which also means the pattern is neither technophobia nor low engagement.

Relationship to learner agency

Learner agency and learner identity are closely related but distinct constructs that are easy to conflate — and both are central to how AI affects learning.

  • Agency is about doing; identity is about being. Learner agency concerns the capacity to act intentionally, make choices, and exercise control over one's learning in the moment — a situated, interactional, and variable capacity. Learner identity concerns who one is and is becoming as a learner — a more durable, narrative, and developmental sense of self. Agency asks "am I able to direct this?", while identity asks "is this who I am / who I want to be?"
  • They are causally intertwined. Agency is both a source and an outcome of identity. Enacting agency — choosing, authoring, persisting — is how a learner comes to see themselves as an agentic person (identity is partly internalized agency). Conversely, a stable disciplinary or professional identity supplies the motivation and self-worth that sustain agency under difficulty. Identity is the accumulated product of repeated agentic acts; agency is the ongoing enactment that builds identity.
  • AI threatens them through different mechanisms. AI can erode agency by inviting over-reliance and passive acceptance — learners stop directing their own reasoning. AI can erode identity by undermining authorship and competence — when AI produces the work, learners may stop feeling the output is "theirs" or that they belong in the domain. The competence paradox is an identity threat; implicit AI redistribution of epistemic labor is primarily an agency threat, though it compounds into identity over time.
  • Safeguarding both is the design goal. Supporting agency means preserving learners' control and choice (e.g., bounded friction, human-in-the-loop oversight). Supporting identity means protecting authorship and recognition (e.g., authentic assessment, transparent attribution of AI versus human contribution). A design that protects agency but not authorship protects control without protecting the sense of self — and vice versa.

In short: foster agency to let learners act; sustain identity so they know who they are while acting. Healthy AI-supported learning attends to both.

Relationship to teacher identity

Learner identity is the student-facing counterpart to teacher identity. Teacher identity — the evolving professional self-understanding of educators — is documented on the Teaching page, where Laidlaw (2026) frame GenAI as an identity crisis for faculty rather than merely a skills gap, and TPK-based teacher training treats identity as part of professional preparation. The two are reciprocal: teachers who experience identity disruption are less able to support their students' identity development, so a healthy AI-integrated system must attend to both.

Connections

Learner identity connects to Learner Agency (identity is enacted through agentic authorship), Self-Efficacy (competence beliefs that sustain identity), Motivation and Self-Determination Theory (identity formation satisfies needs for autonomy and competence), Student Experience and Student Engagement, and STEM Education (where disciplinary identity is a key outcome and predictor of persistence). It is also shaped by Situated Learning and Critical Pedagogy (identity as participation and as power-laden negotiation) and connects to Authentic Assessment (tasks that let learners perform and thus claim an identity). Its Higher Education and K-12 relevance spans both schooling and professional preparation.

Connected Concepts

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Metadata

DisciplineStem Education
LevelAdult Learning
Intended audienceLearners
Foundations of AI in educationLearner Agency, Learner Identity
SourceHistoryEditChat

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