Abstract
Generative AI is reshaping creative practice by expanding what individuals can produce while complicating how effort, authorship, and mastery are understood. This article introduces AI-mediated creative agency (hereafter, mediated agency) as a framework for explaining how human–AI collaboration reshapes creative self-efficacy and the horizons of creative possibility. Mediated agency refers to the experience of acting creatively when intention, process, and outcome are co-produced by human and algorithmic elements, producing new forms of attribution ambiguity, opacity, and uneven control. Extending Bandura’s account of self-efficacy, the framework reconceptualizes his four sources of efficacy information as relational processes interpreted across three dimensions: control over process, authorship, and creative identity. Grounded in sociocultural theory and situated within distributed agency, co-creativity, and technology adoption perspectives, the framework presents testable propositions and educational implications for studying and supporting creative self-efficacy in AI-mediated contexts. Sustaining creative self-efficacy, and over time broader creative confidence, requires attending not only to what AI enables people to produce but to how mediation shapes their experience of contribution, capability, and future creative possibility.
Keywords
Introduction
Human beings create in the realm of the possible, generating ideas, exploring alternatives, and transforming what exists into what could be. This capacity is increasingly exercised with artificial intelligence systems that can propose, elaborate, and refine ideas within seconds. Generative artificial intelligence (AI), therefore, intensifies a longstanding paradox: technology can expand creative capability while also complicating how individuals understand their own contribution, authorship, and agency (Habib et al., 2024).
Generative AI is not the first technology to take part in creative work. Algorithmic tools and computer-assisted design systems have expanded creative possibilities for decades and have already raised difficult questions about authorship and skill (Boden, 2004). What is distinctive about contemporary large-scale generative models is the character of their mediation. These systems can produce open-ended, human-quality outputs across domains, respond dialogically in natural language, and contribute substantive content through processes that are difficult for users to fully trace. This poses a psychological question that existing creativity frameworks have only begun to address: how do creators interpret AI-mediated outcomes as evidence of their own creative capability?
Social cognitive theory, particularly Bandura’s account of self-efficacy, offers a foundational framework for understanding how individuals develop beliefs in their creative capabilities (Bandura, 1977, 2001). Bandura’s model of triadic reciprocal causation states that personal factors, behavior, and environmental influences continuously shape one another, rather than unfolding in a fixed one-way sequence (Bandura, 1986). Self-efficacy, therefore, develops relationally through interactions with environments, people, and tasks. Building on this relational account, the present framework examines what changes when algorithmic systems mediate the relationship among intention, action, and outcome. Such mediation introduces asymmetric power into the configurations through which creative self-beliefs form, complicating the ways individuals interpret mastery, comparison, feedback, and affect as evidence of their own capability.
I propose mediated agency as a framework for understanding creative self-efficacy in human–AI collaboration. Mediated agency refers to the experience of creative action under conditions where intention, process, and outcome emerge through human participation alongside algorithmic contributions, requiring ongoing negotiation of control, authorship, and identity. Throughout this article, mediated agency is used as shorthand for AI-mediated creative agency: the configuration of creative self-efficacy formation that emerges when human creative action is mediated by generative AI.
The framework integrates psychological, sociocultural, distributed, and possibility-oriented perspectives. Distributed approaches conceptualize creative work as emerging from relations among people, tools, and contexts rather than from isolated individuals (Enfield & Kockelman, 2017; Glăveanu, 2013; Sawyer & DeZutter, 2009). Mediated agency builds on this relational view, focusing on how individuals within such systems develop, maintain, or lose beliefs in their own creative capability. From a possibility studies perspective, mediated agency brings the developmental horizon into view by asking how AI-mediated creative work shapes the futures creators perceive as attainable, the forms of effort they come to value, and the kinds of creative selves they can imagine becoming (Glăveanu, 2013; Glăveanu, 2021).
Mediated agency is positioned in relation to several adjacent traditions, including co-creativity and creativity-support frameworks (Shneiderman, 2000), human-centered AI (Shneiderman, 2020), technology acceptance perspectives (Davis & Granić, 2024), sociocultural mediation (Vygotsky, 1978; Wertsch, 1991), and distributed agency (Enfield & Kockelman, 2017). The sections that follow clarify how mediated agency draws on these traditions while shifting the focus to the formation of creative self-efficacy in AI-mediated creative work.
The framework makes three contributions. First, it specifies how Bandura’s four sources of self-efficacy operate as mediated relational processes shaped by human–AI interaction. Second, it identifies three key dimensions of negotiation: control over process, authorship, and creative identity, along with the developmental trajectories these negotiations can produce. Third, it connects these psychological dynamics to possibility studies by showing how AI may simultaneously expand immediate creative possibilities and constrain the developmental possibilities necessary for sustainable creative agency. The sections that follow develop the theoretical grounding for these contributions, beginning with the psychological and relational foundations on which the framework builds.
Background
Creative Self-Beliefs
Creative self-efficacy refers to individuals’ beliefs in their ability to engage in creative activities within specific domains or situations (Tierney & Farmer, 2011). Creative confidence denotes a broader, more generalized sense of oneself as a creative person and as someone willing to take risks in creative situations (Karwowski, 2016; Kelley & Kelley, 2012). These beliefs influence how individuals approach creative problems, how persistently they engage with them, and how they interpret success and failure (Wojtycka et al., 2025). Creative self-efficacy and confidence are related constructs but not interchangeable: self-efficacy shapes moment-to-moment judgments about capability in specific domains or tasks, while confidence influences whether individuals choose to engage in creative work at all and how they narrate themselves as creative people across contexts. Over time, task-specific mastery experiences can accumulate into broader creative self-beliefs, so increases in self-efficacy can support the development of creative confidence (Beghetto, 2006; Beghetto et al., 2021).
In human-AI creative contexts, the most immediate psychological effects often appear at the level of self-efficacy. AI can increase or decrease perceived capability depending on how individuals interpret their contributions relative to the system and on how success is attributed (Newman et al., 2024). Recent research on general and AI-specific creative self-beliefs similarly suggests that AI contexts may differentiate the ways people evaluate their creative capacities rather than increase or decrease a single global belief (Faiella et al., 2025). These shifts can subsequently influence creative confidence and creative identity by altering individuals’ sense of themselves as capable creators across contexts.
Bandura’s self-efficacy theory identifies four primary sources of efficacy information: mastery experiences, vicarious learning, social persuasion, and affective states (Bandura, 1977). In many traditional creative practices, the relation between intention, action, and outcome is sufficiently transparent that individuals can treat successes and failures as meaningful evidence of their own capability.
Generative AI and Self-Efficacy
Generative AI introduces new forms of mediation that complicate the development of creative self-beliefs (Habib & Gardiner, 2026; Newman et al., 2024; Worrall & Collins, 2024). Although many technologies involve processes that are not fully transparent, generative AI can make it especially difficult to trace how human intentions, prompts, selections, and revisions shape the final result (Marrone et al., 2026). This can introduce attribution ambiguity: creators may struggle to determine whether successful work reflects their own capabilities, the system’s contribution, or some combination of both.
Empirical evidence illustrates the psychological effects. Students using generative AI as a brainstorming partner on creative tasks reported that AI was helpful, yet simultaneously reported decreased confidence in their own creative abilities; they could produce outputs with AI assistance but felt less capable as independent creators (Habib et al., 2024). Children who collaborated with AI for storytelling reported heightened self-efficacy during AI-supported activities but experienced difficulty when asked to perform without AI support (Newman et al., 2024). Professional creatives described tensions between empowerment and loss of authorship when AI-generated ideas closely resembled professional work (Worrall & Collins, 2024). These findings suggest that AI reshapes the experiential conditions under which self-efficacy is formed, particularly by decoupling performance outcomes from internalized capability.
A Relational Perspective on Creative Agency
To account for these transformations, it is useful to consider sociocultural, ecological, and distributed approaches that conceptualize creativity as emerging from relations rather than residing solely within individuals. Frameworks such as the Five A’s model (Glăveanu, 2013) and distributed agency theories describe creativity as a sociomaterial process in which actors, artifacts, and audiences co-constitute creative outcomes (Enfield & Kockelman, 2017; Sawyer & DeZutter, 2009). These accounts establish that creativity is not located solely in individuals but emerges through the configuration of relations among actors, tools, artifacts, audiences, and environments. Mediated agency builds from this premise by asking a complementary psychological question: how do creators experience themselves as capable agents within such distributed systems?
The Five A’s framework provides an entry point for understanding what AI mediation changes. AI systems alter the Actor by expanding what is technologically possible; they transform the Action by introducing generative participation that both expands possible directions and complicates the traceability of human intention; reshape Artifacts by producing outputs that blur the boundary between human and algorithmic contribution; modify the Audience by shifting evaluative standards toward AI-augmented benchmarks; and restructure Affordances by reconfiguring what creative moves are available, efficient, or legible within a given context. Distributed approaches illuminate these transformations across the relational field. A complementary question concerns how these transformations are experienced subjectively: how they register in the creator’s formation of self-beliefs and sense of possibility.
Distributed agency and mediated agency address complementary analytical questions. Distributed agency examines how creative agency is configured across sociomaterial systems; mediated agency examines how individuals within those systems interpret AI-mediated creative outcomes as evidence of their own capability. The former provides a relational account of creative action, while the latter focuses on the lived formation of creative self-beliefs within that relational field.
Mediation in Sociocultural Theory
The concept of mediation in this framework is grounded in sociocultural theory, particularly Vygotsky’s (1978) argument that human cognition develops through the use of culturally produced tools and signs. For Vygotsky, higher mental functions such as reasoning, memory, planning, and self-regulation do not emerge in isolation within the individual mind. Rather, they are formed through social interaction and are mediated by psychological tools, including language, writing, counting systems, diagrams, symbols, and other culturally available means.
These tools actively shape the structure and possibilities of thinking itself. Language, for example, does more than communicate preexisting ideas; it reorganizes attention, supports abstraction, and enables forms of reflection that would not be possible in the same way without it. Wertsch (1991) extended this insight through mediated action theory, arguing that human agency is always exercised through cultural tools rather than independently of them. From this perspective, tools participate in action by shaping what individuals can perceive, imagine, decide, and accomplish. Mediation, therefore, can be understood as a constitutive process through which human thought and agency are formed, transformed, and enacted.
The term mediated agency has prior uses in adjacent literatures that this framework both draws upon and extends. In sociocultural psychology, Wertsch and colleagues used the concept to describe agency as enacted through mediational means, arguing that agency is not a property of the isolated individual but is constituted through the cultural tools and signs that structure action (Wertsch et al., 1993). This formulation, rooted in Vygotsky’s account of psychological tool use, establishes the foundational principle that mediational means actively reshape the structure and meaning of action. A related body of work in human factors and automation research shows that machine-mediated action can alter users’ subjective sense of control. For example, Berberian et al. (2012) found that the level of automation affects both implicit and explicit measures of agency, suggesting that technological mediation can reshape the experienced relationship among intention, action, and outcome.
The proposed framework here builds on these traditions while advancing a psychologically distinct application: rather than analyzing how tools configure action or how automation disrupts motor control, it examines how algorithmic mediation reorganizes the experiential and interpretive processes through which creative self-beliefs form. Mediated agency, as used here, is therefore not a departure from these prior accounts but an extension of their core insight: agency is constituted through its mediational conditions.
When AI mediates between intention and outcome, it can reshape how intention is formed, how effort is apportioned, and how outcomes are interpreted, with direct consequences for the formation of self-efficacy. A critical departure from many earlier mediational tools, however, is the combination of opaque operation and generative participation. Many tools shape creative action in ways that are not fully transparent, but generative AI can introduce substantive content into the emerging artifact, sometimes in directions that diverge from the user’s intention. This can expand possibility spaces by surfacing unexpected directions, alternatives, and forms of expression that creators might not have generated on their own. At the same time, it can make it harder to trace the relationship among intention, action, and outcome, with direct consequences for how creators interpret their own contributions and capabilities. Mediated agency offers a vocabulary for examining what happens to Bandura’s sources of self-efficacy when the mediational means not only support creative action but also contribute substantive content whose relation to human intention can be difficult to trace.
Mediated Agency
When AI systems are included in creative production, they become part of the relational conditions from which creative self-efficacy beliefs emerge. The four sources of self-efficacy, mastery experiences, vicarious learning, social persuasion, and physiological and affective states, are no longer interpreted only through direct human action. They are filtered through three sites of negotiation: control over process, authorship, and creative identity. These sites shape whether AI-mediated experiences become credible evidence of capability or produce uncertainty about contribution, responsibility, and growth. Within this framework, agency remains fundamentally human. AI systems do not possess independent intentions in the sense relevant to self-efficacy; rather, they mediate the conditions under which humans interpret creative action (Figure 1).
AI-mediated creative agency framework.
The figure illustrates how Bandura’s four sources of self-efficacy are interpreted through AI-mediated creative agency. AI mediation reshapes mastery experiences, vicarious learning, social persuasion, and physiological and affective states through three central sites of negotiation: control over process, authorship, and creative identity. These processes influence creative self-efficacy and, over time, broader creative confidence, while supporting different developmental trajectories: augmentation, dependence, or displacement.
The developmental consequences of these negotiations can produce distinct trajectories for creative self-efficacy over time. For example, augmentation can occur when mediated agency preserves or strengthens creative self-efficacy by maintaining meaningful control, clear authorship, and stable creative identity. AI scaffolds capability without replacing it, and creators develop transferable confidence alongside expanded productive capacity. Dependence can occur when self-efficacy becomes calibrated to AI-supported performance rather than independent capability, producing confidence that appears robust in mediated contexts but fails to transfer when AI support is removed or unavailable. Displacement can occur when repeated mediation progressively erodes both creative identity and self-efficacy, as creators increasingly attribute creative outcomes to the system rather than to their own contribution.
These trajectories are not fixed or predetermined; they represent possible patterns shaped by design choices, pedagogical contexts, and individual navigation strategies. The framework’s central claim is that the trajectory an individual follows depends on the quality of mediation experienced across the four sources of self-efficacy and three dimensions, specifically, whether mediation preserves or erodes the conditions for genuine mastery, clear authorship, and stable creative identity.
Negotiation, Power, and Psychological Outcomes
The relationship between humans and AI is not symmetrical. AI systems carry algorithmic authority: users often perceive their outputs and evaluations as objective or neutral, which grants these systems disproportionate influence over creative decisions (Kern et al., 2022; Shirky, 2010). This creates an uneven power dynamic in which AI can shape outcomes and evaluative standards in ways that may diverge from users’ intentions or developmental needs.
Algorithmic authority operates within power structures that configure how mediated agency unfolds. Training data can embed cultural and aesthetic biases that privilege certain styles or ideas (Liu, 2023). In educational and professional contexts, institutional policies ranging from prohibitions to mandated adoption create distinct psychological conditions that shape how creative self-efficacy develops (Fawns, 2022). These institutional choices sit within broader layers of power, from relationships with specific algorithms to dependence on platforms and employers, which shape how creators can negotiate authorship and control (Nguyen & Mateescu, 2024).
Mediated agency should therefore be viewed as an ongoing negotiation in which humans must actively maintain creative authority against both algorithmic influence and institutional pressures. The degree to which individuals succeed in this negotiation directly affects their creative self-efficacy, as it determines whether they experience authentic control over the process, credible authorship of outcomes, and a stable sense of creative identity within human-AI collaboration. The framework, therefore, captures both the collaborative potential and the contested nature of these partnerships, emphasizing that creative confidence grows when collaboration preserves meaningful agency and diminishes when collaboration displaces control or obscures contribution.
Mediated Mastery Experiences
Mastery experiences provide the most influential source of self-efficacy because they offer direct evidence of capability (Bandura, 1977). In AI-mediated contexts, individuals can achieve impressive creative outcomes, potentially accomplishing tasks they could not complete alone, which constitutes a genuine form of mastery: mastery of human-AI collaboration. However, when success cannot be clearly attributed to one’s own effort and decision-making, the ability to build self-efficacy can be weakened.
Attribution ambiguity is central here. When creators ask, “Did I do this, or did the AI?” they may struggle to treat AI-supported success as evidence of personal capability. In AI-mediated creative work, this can produce a form of pseudo-competence, or miscalibrated self-efficacy: a superficial sense of capability that arises when individuals succeed in supportive conditions without developing the underlying skills required for independent performance. Pseudo-competence is especially likely when impressive outputs are achieved primarily through algorithmic contribution rather than the creator’s own conceptual development, decision-making, or technical mastery. As a result, these successes may not provide reliable evidence of transferable capability.
AI can also weaken mastery experiences when it removes too much of the challenge through which capability is normally tested. Mastery experiences are most potent when creators encounter difficulty, make decisions, revise, and eventually succeed (Csikszentmihalyi, 1988; Young et al., 2024). When AI collapses this process into rapid output generation, creators may produce impressive artifacts without developing equally robust beliefs in their own transferable capability.
However, AI can support genuine mastery when it scaffolds rather than replaces creative processes. Systems that prompt reflection on creative decisions, require iterative refinement, or provide partial solutions that still demand substantial human judgment can facilitate enduring skill development. When creators understand how their decisions guide or constrain AI outputs and can trace the connection between their intentions and the results, mediation can support rather than undermine the accumulation of robust mastery experiences. The key distinction lies in whether AI handles mechanical execution while preserving conceptual challenge, or collapses both dimensions, making creators selectors rather than makers.
Mediated Vicarious Learning
Vicarious learning occurs when individuals observe others similar to themselves succeed and infer that they too possess the capability to achieve similar success (Bandura, 1997). The effectiveness of vicarious learning depends on the perceived similarity between the observer and the model (J. V. Wood, 1996). In AI-mediated contexts, creators observe both AI systems and peers producing impressive AI-augmented outputs, which complicates the identification of appropriate comparison targets.
A first challenge is that AI is not a “similar other.” AI-generated outputs may function as examples or benchmarks, but they do not model the human process of struggling, revising, and developing skill in the way a peer or mentor can. Observing an algorithm generate fluent text or polished designs may expand a sense of what is possible, but can also trigger feelings of inadequacy when human efforts seem slow or clumsy by comparison (Habib et al., 2024). A second challenge is the “double comparison” problem: creators might compare themselves both to AI outputs and to peers whose work is enhanced by AI support. Emerging work on AI and art self-image assessment suggests that exposure to AI-generated or AI-augmented outputs can obscure how individuals assess their own creativity, even when their performance improves (Kellerwessel & Ujhelyi, 2024).
Social comparison theory indicates that comparisons to highly superior standards, particularly in domains central to self-concept, can threaten rather than motivate (Festinger, 1954; Tesser, 1988). In AI-mediated creative work, the evaluative space increasingly encompasses AI outputs and AI-augmented performances, making it difficult to distinguish between developments in human capability and the appearance of capability through technological augmentation.
Vygotsky’s (1978) concept of the zone of proximal development offers additional insight into when AI-generated outputs serve as productive models rather than discouraging comparisons. AI outputs may serve as demonstrations that lie within or beyond an individual’s developmental reach. When outputs fall within the zone of proximal development, representing what learners could achieve with appropriate guidance, they can expand their capabilities through supported exploration and by providing aspirational models. However, when AI-generated work dramatically exceeds a creator’s current capability, falling outside their zone of proximal development, it may instead induce comparisons that feel threatening rather than motivating. This suggests that the developmental appropriateness of AI-generated models can significantly affect self-efficacy, and that creators at different skill levels may require different relationships with AI outputs to maintain confidence as they develop capability.
Mediated agency emphasizes that the psychological impact of these comparisons depends on how AI outputs are framed within a creator’s identity and practice. When AI is framed as a tool that extends human possibility, AI-generated examples can serve as inspirational models for exploration. When AI is framed—or experienced—as a replacement or as an unreachable standard, vicarious exposure can undermine self-efficacy and destabilize creative identity.
Mediated Social Persuasion
Social persuasion involves feedback and encouragement from others that shape self-efficacy by signaling capability and potential (Bandura, 1997). In creative contexts, feedback from teachers, mentors, and peers carries weight when it comes from credible, trusted sources (Hattie & Timperley, 2007). AI participation transforms this source by introducing automated feedback that often appears objective yet lacks contextual grounding.
AI feedback can carry an illusion of objectivity because it emerges from data-driven analysis rather than overtly subjective human judgment. Creators may treat AI-generated evaluations or recommendations as more authoritative, even when the underlying models encode biases or lack contextual sensitivity. At the same time, many AI systems are designed to be supportive, sometimes providing overly positive assessments to maintain engagement, which can erode trust in their feedback as an authentic indicator of capability (Kern et al., 2022; O’Hara, 2021).
Complexities arise when AI feedback is mediated through human instructors or peers. A teacher might rely on AI analysis to identify strengths and weaknesses in student work, then present that feedback in human terms. In such cases, the persuasive impact depends on whether creators perceive the human source as genuinely endorsing the feedback or merely relaying algorithmic judgments. When a mentor’s encouragement is informed by AI analysis, does that encouragement carry the same persuasive weight as feedback based on the mentor’s own judgment? If creators know that praise or critique was algorithmically generated, even if delivered by a human intermediary, how does this knowledge affect the feedback’s impact on self-efficacy?
Effective social persuasion depends on clarity about the source, the authenticity of the relationship, and a genuine belief in the individual’s potential, qualities that algorithmic systems cannot provide in the same relational sense as teachers, mentors, or peers. Feedback practices that clarify the role of AI, foreground human judgment, and situate evaluation within ongoing relationships are more likely to support creative self-efficacy than practices that delegate persuasion to automated systems without clear human interpretation or responsibility.
Mediated Affective States
Physiological and affective states function as sources of self-efficacy information, as individuals interpret emotions such as excitement, frustration, anxiety, or satisfaction as cues about their capability (Bandura, 1997). Creative work traditionally involves a mixture of challenging emotions and satisfying breakthroughs, with periods of productive struggle contributing to a sense of growth and resilience (Csikszentmihalyi, 1997).
AI mediation introduces new affective patterns. Creators may experience excitement when AI generates surprising possibilities and flow-like engagement when collaboration feels responsive; related research on technology-mediated learning and creative confidence suggests that affective and metacognitive experiences matter for self-efficacy and self-beliefs (Smit et al., 2025; Wojtycka et al., 2025). At the same time, they experience frustration when AI misinterprets intentions, anxiety when AI outputs appear superior to their own, and discomfort when the boundary between human and machine contribution becomes unclear (Worrall & Collins, 2024).
A particularly important shift is the emergence of “too easy” creative experiences. When AI dramatically reduces effort and uncertainty, work can feel effortless but also emotionally flat, depriving creators of the satisfaction that comes from overcoming difficulty. Struggle provides evidence of emotional engagement, boundary pushing, and skill development. When AI eliminates this struggle by generating solutions immediately, the emotional texture of creative work changes: impressive results become achievable without the sustained effort that has traditionally been registered as evidence of capability and growth. This creates a paradox: creators can produce compelling work while feeling emotionally empty about the process, a pattern that, if sustained, prevents those affective cues from accumulating into creative identity.
Mediated agency emphasizes that individuals must learn to interpret these novel affective states in ways that support, rather than undermine, self-efficacy. Design and pedagogical practices that intentionally reintroduce manageable challenges, encourage reflection on emotional responses to AI-assisted work, and validate struggle as integral to creative development can help recalibrate affective cues in AI-mediated environments.
Dimensions of Mediated Agency: Control, Authorship, and Creative Identity
The four sources of self-efficacy operate across three key dimensions that constitute ongoing sites of negotiation in human-AI creative collaboration. These dimensions shape how mediated mastery, vicarious learning, persuasion, and affect are experienced and interpreted. When creators cannot determine how much control they exercised, what contribution they can claim, or how AI-mediated work fits their creative identity, self-efficacy becomes harder to stabilize. In this framework, authorship refers to both formal ownership of a creative product and the creator’s ability to identify, claim, and explain their contribution in hybrid human-AI work.
Control Over Process
Control refers to the extent to which creators can direct, interrupt, and revise AI contributions in ways that feel responsive to their intentions. Interactive AI interfaces invite a sense of control through prompting and parameter adjustments, yet the underlying generative mechanisms remain inaccessible, creating a structured form of control in which creators have real but bounded influence over outcomes. This differs from the illusion of control (Langer, 1975), which was formulated for chance situations in which individuals exert no actual influence yet believe otherwise. In AI-mediated creative work, influence is genuine but partial: creators direct outputs through prompting, selection, and revision, yet they cannot fully inspect the mechanisms linking their intentions to the resulting artifact. The psychological effect is significant. When the path from intention to outcome is difficult to trace, successful outputs are less readily internalized as evidence of personal capability. Research on algorithmic authority suggests that this interpretive difficulty is both cognitive and social, shaped by the perceived objectivity and authority attributed to AI systems (Kern et al., 2022; O’Hara, 2021).
The negotiation of control involves balancing two risks. Ceding too much control can lead to AI-dominated processes in which human decision-making is reduced to accepting or rejecting suggestions. Exercising excessive control by over-constraining the system can render AI contributions shallow or merely decorative, limiting opportunities for genuine co-creation. Interfaces that surface model uncertainty, provide stepwise explanations, or allow users to iteratively refine outputs with clear traceability can support more meaningful control.
Within mediated agency, control over process is a key dimension through which mediated mastery, vicarious learning, persuasion, and affect are interpreted. When creators experience themselves as actively directing AI contributions, successes, and failures are more readily integrated as evidence of their own capability, supporting creative self-efficacy.
Authorship
Authorship concerns who is perceived as responsible for creative work and which contributions can be legitimately claimed. In human-AI collaboration, authorship becomes ambiguous when ideas, structures, or stylistic elements originate from AI prompts or outputs and are not clearly distinguished from human input.
This ambiguity has direct implications for self-efficacy. If creators cannot identify which aspects of a product reflect their decisions, skills, and efforts, success provides weak evidence of personal capability. Consider a student using AI to help write an essay. The final text reflects both the student’s ideas and the AI’s elaborations. Which sentences express the student’s thinking? Which phrases came from the AI’s training data? If the overall argument structure emerged from AI suggestions, but the student selected which points to develop, who authored the argument?
Professional creatives encounter similar attribution challenges. Synthesizing recent empirical work, Habib and Gardiner (2026) report that designers, writers, and other creative professionals who use generative AI often describe their role shifting from constructing work directly to curating, editing, or refining system-generated options, which can blur the boundary between their own expertise and the system’s contribution. In these cases, highly polished outputs may be achieved, but creators are less certain which aspects of the work genuinely reflect their spatial reasoning, aesthetic judgment, or conceptual development. As a result, even when performance improves, it can be harder for professionals to treat AI-supported successes as clear evidence of their own creative capability.
Mediated agency emphasizes practices that preserve authorship as a source of mastery and identity. These include tracking the provenance of AI-generated elements, encouraging transparent disclosure of AI involvement, and incorporating reflective activities in which creators articulate their contributions, intentions, and rationales. Such practices make it easier for both creators and evaluators to recognize human agency within hybrid outcomes, strengthening the link between performance and self-beliefs.
Creative Identity
Creative identity refers to the centrality of creativity to one’s sense of self and to the narratives through which individuals understand themselves as creative people (Glăveanu & Tanggaard, 2014; Winner, 1982). It develops through sustained creative practice, recognition by others, and the integration of creative experiences into broader life stories (Beghetto et al., 2021; Csikszentmihalyi, 1984). When creative identity is secure, temporary setbacks or transitions do not fundamentally threaten one’s sense of being a creator.
AI mediation introduces new threats and opportunities for creative identity. When technologies perform tasks that previously signaled creative expertise, individuals may question what remains distinctly human in their practice. Encountering AI systems that appear more fluent or technically proficient can provoke feelings of inadequacy, particularly for those whose identities are deeply tied to specific creative skills. Others may adopt new identities as orchestrators, editors, or conductors of human-AI ensembles, redefining what it means to be creative.
From the perspective of mediated agency, sustaining creative identity under AI mediation involves recognizing and foregrounding dimensions of creativity that remain irreducibly human: problem framing, value judgment, contextual adaptation, ethical reflection, and the integration of creative work into meaningful life narratives. Educational and professional practices that acknowledge these dimensions and help individuals narrate themselves as creative agents in relation to AI can support the continuity and renewal of creative identity.
Possibility Spaces Under AI Mediation
Creative self-efficacy is shaped not only by past experiences but also by perceptions of what actions and futures are attainable. From a possibility studies perspective, mediated agency clarifies how technologies configure which creative futures feel reachable, which feel effortless, and which recede from view (Glăveanu, 2021). Glăveanu’s account of creative possibility is not purely a field of options arrayed before the creator; it is also temporal. Possibilities are shaped by what has been done, what is being done, and what the creator is becoming through practice. The developmental stakes of mediated agency are therefore about who creators are becoming as creative agents over time. When generative AI becomes part of sociomaterial practices of making, experimenting, and struggling, it reshapes the horizons of the possible by altering what creators perceive as thinkable, appropriate, or worth pursuing.
Generative AI shapes these horizons through several interrelated mechanisms. By offering ready-made alternatives derived from prior data, AI can anchor creative thinking around familiar or statistically likely options, orienting attention even when suggestions are rejected. The speed and polish of AI outputs can reset expectations about what counts as adequate creative performance, particularly for novices, while training data biases can reinforce dominant aesthetic norms and narrow perceptions of creative legitimacy. These dynamics do not eliminate human agency. Creators still accept, modify, or reject AI contributions, but the systems structure the field of cognitive availability, making some creative directions feel natural and others increasingly remote. When AI mediation scaffolds impressive outputs without requiring the intentional effort and iterative refinement through which creative identity develops, it may expand what seems achievable in the present while constraining what remains possible for the developing self.
Developmental Pathways
The developmental dynamics of mediated agency involve complex interactions between immediate performance gains and longer-term self-efficacy outcomes. When AI scaffolding enables immediate success, individuals may experience temporary increases in perceived capability. However, when mediation obscures the link between intention, effort, and outcome, these short-term gains may fail to accumulate into durable creative self-efficacy or broader creative confidence—a pattern consistent with research on scaffolding dependence in learning environments (D. Wood et al., 1976).
Sustainable self-efficacy development requires experiences that preserve effort, authorship, and credible attribution. The three possible trajectories introduced earlier, augmentation, dependence, and displacement, describe the range of developmental outcomes these patterns can produce. Augmentation requires that mediation actively preserve the conditions for genuine capability development; dependence emerges when convenience and quality displace those conditions; displacement occurs when the cumulative effect is a loss of creative self-belief and identity.
Expertise likely moderates these developmental trajectories. Novices may rely more heavily on algorithmic scaffolding and experience greater difficulty maintaining attribution clarity, whereas experts with established creative schemas may more effectively integrate mediation into existing creative strategies (Chi et al., 2014). Metacognitive awareness—the capacity to monitor and regulate one’s own cognitive processes—may also buffer against negative effects by helping creators maintain clarity about their contributions even within mediated systems (Schraw & Dennison, 1994).
At the same time, AI expands perceived possibilities in ways that can genuinely enhance creative engagement. Across domains, individuals report increased willingness to attempt complex creative tasks when AI scaffolding is available, suggesting that mediated mastery experiences can broaden creative self-efficacy and creative identity (Beghetto, 2006; Newman et al., 2024). However, evidence also indicates that these expanded possibilities may be fragile when confidence is tied primarily to AI-supported performance rather than transferable creative capability. This tension between expanded short-term possibility and reduced longer-term developmental openness points to a central implication of mediated agency: sustaining creative self-efficacy under AI mediation requires preserving indeterminacy, struggle, and authorship as conditions of possibility, rather than allowing efficiency and polish to crowd them out.
Implications
The framework has implications for creativity research, education, and professional practice. For research, mediated agency shifts the central question from whether AI increases or decreases creative performance to how specific configurations of human-AI interaction shape the psychological conditions that support creative self-efficacy. This requires attention to attribution clarity, degrees of user control, authorship, and the temporal stability of confidence developed through AI-supported work. Researchers should also examine individual and contextual moderators, such as creative identity centrality, mindset, domain expertise, and institutional norms, to understand why similar AI tools produce divergent self-efficacy outcomes across populations and domains. Together, this work can help position creative self-efficacy as a relational and situational construct shaped by sociotechnical conditions rather than by individual skill alone.
For education and professional practice, the implications center on preserving the conditions under which creative agency and confidence can develop sustainably. In educational contexts, AI integration should be guided by pedagogical designs that maintain meaningful student agency, make human contribution visible, and preserve productive struggle as a core feature of creative learning. This involves balancing AI-assisted and independent work, emphasizing process-oriented assessment, and supporting learners’ capacity to reflect on how mediation shapes their creative decisions. In professional and organizational settings, similar principles apply. AI tools should be implemented in ways that reinforce rather than displace human judgment, authorship, and expertise, supported by leadership practices, ethical guidelines, and AI literacy that frame technology as augmentative rather than substitutive. Across contexts, the central practical implication is not whether to adopt AI, but how to configure human-AI relations so that expanded creative possibilities do not come at the cost of creative self-efficacy and long-term creative development.
Future Research
Future research should operationalize key constructs and test specific mechanisms through which AI mediation shapes self-efficacy. Attribution clarity can be measured using self-report items assessing the extent to which individuals can identify their contribution to the final output, informed by work on creative agency and agentic action (Karwowski & Beghetto, 2019). Control can be experimentally manipulated by varying the transparency and override capacity of AI systems, following principles from interactive systems design research (Norman, 2013).
The framework generates four testable propositions:
Longitudinal studies could track developmental trajectories across expertise levels and examine which design and pedagogical conditions steer individuals toward augmentation rather than dependence or displacement. Cross-domain research could examine how mediated agency operates across creative fields with different material practices, evaluation traditions, and tool ecologies. Cultural research could explore how mediated agency functions in contexts where creativity, individuality, and technology are understood differently.
As AI capabilities continue to advance, empirical work will need to look beyond what can be made to how creative agents and their sense of possibility develop under mediation. Mediated agency neither celebrates AI as an unqualified solution nor condemns it as an existential threat. Instead, it provides a framework for understanding how technological mediation restructures the lived experience of creative possibility, enabling more intentional navigation of human-AI collaboration toward futures where creators develop robust, transferable creative confidence alongside impressive productive capacity.
Conclusion
Mediated agency should be understood as a conceptual framework rather than a fully developed theory. It organizes emerging empirical patterns and offers mechanisms that can be operationalized and tested as research on human–AI collaboration advances. The goal is to provide an approach for analyzing how technological mediation shapes the development of creative self-efficacy and related psychological processes. As evidence accumulates, elements of the framework can be refined, expanded, or revised through empirical study.
The framework offers three theoretical contributions. First, it specifies self-efficacy as a relational construct within AI-mediated systems, attending to the subjective experience of agency in human–AI collaboration. Rather than treating individual psychology and sociocultural relations as separate domains, it shows how beliefs about creative capability crystallize from participation in technologically mediated relational systems. Second, it identifies control, authorship, and creative identity as specific sites where creative agency is negotiated in AI-mediated work, providing analytic categories for understanding when and how technological mediation supports or undermines creative self-efficacy. Third, it connects these psychological dynamics to possibility studies by showing how AI simultaneously expands immediate creative possibilities and constrains the developmental possibilities necessary for sustainable creative agency. It also introduces augmentation, dependence, and displacement as developmental trajectories through which AI-mediated creative self-efficacy may unfold.
These contributions address a gap in existing creativity research. Distributed agency frameworks analyze how creativity emerges across networks of actors and artifacts (Glăveanu, 2013; Glăveanu, 2021); mediated agency complements these accounts by focusing on phenomenological experience. Psychological approaches to creative self-efficacy offer insight into individual confidence but have only begun to be adapted to contexts in which algorithmic systems actively participate in creative work. Mediated agency bridges these perspectives by examining how individuals experience their own creative capability within relationally constituted human-AI systems, and by grounding the concept of mediation in a sociocultural tradition that treats tools as active reorganizers of cognition rather than neutral assistants.
Future research should test mediated agency empirically and identify design considerations and educational practices that actively cultivate augmentation trajectories. This requires interdisciplinary collaboration across creativity science, educational psychology, human-computer interaction, and AI ethics. The goal is to create sociotechnical environments in which individuals develop enduring creative self-efficacy rather than dependence on algorithmic fluency. In such environments, creative confidence is built through genuine mastery, clear contribution, and a stable sense of creative identity, while the horizons of creative possibility expand without leaving the developing self behind.
Footnotes
Funding
The author received no financial support for the research, authorship, and/or publication of this article.
Declaration of Conflicting Interests
The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.