Is this project an undergraduate, graduate, or faculty project?
Graduate
Project Type
individual
Campus
Daytona Beach
Authors' Class Standing
Zoe Spanos, Graduate student
Lead Presenter's Name
Zoe Spanos
Lead Presenter's College
DB College of Arts and Sciences
Faculty Mentor Name
Dr. Chad Tossell
Abstract
Artificial intelligence (AI) is increasingly embedded into adult learning and higher education, serving not only as a support tool for cognitive aid but also as a system that can shape how learners regulate their motivation and engagement. This presentation examines how the use of AI in adult learning contexts may support or undermine self-determined motivation, drawing from Self-Determination Theory (SDT). It is a conceptual paper that draws on existing literature and theoretical analysis, examining AI reliance from minimal use to full automation across SDT's three basic psychological needs: autonomy, competence, and relatedness. Motivation is essential for learning, but the quality is just as important as its presence. One issue with integrating AI into adult learning is the potential for metacognitive laziness and cognitive surrender. In this process, learners can offload not only the direct task-related cognitive demands but also the self-regulatory processes involved in learning, which can influence motivation. To address this, the paper introduces the construct of motivational outsourcing. Here, the impact of AI on motivation is explored in terms of the deliberate or habitual delegation of goal initiation, volitional regulation, and competence development. Several key aspects are explored throughout, including whether short-term performance gains come at the cost of long-term learning, whether AI reflects genuine autonomy or just the appearance of choice, and whether AI can substitute for human connection in learning. The paper proposes a conceptual model in which AI design features influence satisfaction needs, which shapes the quality of motivation achieved, with motivational outsourcing playing a role in how effects occur. Applications are discussed as recommendations for educators, AI designers, and organizations. For AI to support adult learners, it must be designed not only to improve performance but to protect the motivational capacities of adult learners rather than replacing them.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
Included in
Artificial Intelligence and Robotics Commons, Education Commons, Human Factors Psychology Commons
Motivational Outsourcing: AI, Self-Determination, and the Changing Nature of Adult Learning
Artificial intelligence (AI) is increasingly embedded into adult learning and higher education, serving not only as a support tool for cognitive aid but also as a system that can shape how learners regulate their motivation and engagement. This presentation examines how the use of AI in adult learning contexts may support or undermine self-determined motivation, drawing from Self-Determination Theory (SDT). It is a conceptual paper that draws on existing literature and theoretical analysis, examining AI reliance from minimal use to full automation across SDT's three basic psychological needs: autonomy, competence, and relatedness. Motivation is essential for learning, but the quality is just as important as its presence. One issue with integrating AI into adult learning is the potential for metacognitive laziness and cognitive surrender. In this process, learners can offload not only the direct task-related cognitive demands but also the self-regulatory processes involved in learning, which can influence motivation. To address this, the paper introduces the construct of motivational outsourcing. Here, the impact of AI on motivation is explored in terms of the deliberate or habitual delegation of goal initiation, volitional regulation, and competence development. Several key aspects are explored throughout, including whether short-term performance gains come at the cost of long-term learning, whether AI reflects genuine autonomy or just the appearance of choice, and whether AI can substitute for human connection in learning. The paper proposes a conceptual model in which AI design features influence satisfaction needs, which shapes the quality of motivation achieved, with motivational outsourcing playing a role in how effects occur. Applications are discussed as recommendations for educators, AI designers, and organizations. For AI to support adult learners, it must be designed not only to improve performance but to protect the motivational capacities of adult learners rather than replacing them.