Episode 1: Rethinking Agency in the Age of AI: Gaining an Initial Understanding
Authors: Teddy Kim, James Basham, Ph.D., and Angelica Fulchini Scruggs, Ph.D.; info@ciddl.org
[embedyt] https://www.youtube.com/watch?v=LgV5AsxkKCo[/embedyt]
Artificial intelligence (AI) has rapidly emerged as a transformative force in education, offering personalized learning, automation, and new forms of engagement. However, beneath these promises lies a fundamental question: How does AI influence human agency, particularly for students with disabilities (SWD)?
As one participant from CIDDL AI Office Hours insightfully mentioned, “When I ask researchers who claim that AI improves student learning to explain how it happens and what evidence supports that claim, they usually pause, unsure of the specifics, and then scratch their heads.” This observation highlights a critical gap in the field.
AI holds the potential to enhance the quality of life for all students. If, and only if, we understand how it affects their ability to act purposefully, make choices, and direct their learning. However, this concerns human agency and the roles humans and machines take in the experience. This series will explore the understanding the research community currently has about these roles. But first, what is human agency?
Human agency refers to the capacity of individuals to act independently, make their own free choices, and influence their own lives and environments. It involves intentionality, self-reflection, and the ability to initiate actions toward goals.
To start this, this episode draws on three key theoretical frameworks:
Human Agency: Bandura’s Social Cognitive Theory
Human agency (Bandura, 1989) emphasizes that individuals are not passive recipients of events. Instead, they influence their lives through forethought, self-efficacy, and self-reflectiveness. This theory focuses on belief systems and how individuals perceive their ability to influence outcomes.
For instance, AI can support students in self-monitoring and adjusting their strategies. In this context, AI can positively influence students’ self-efficacy by helping them identify effective learning methods, track progress, and make informed decisions about their academic efforts. However, suppose students rely too heavily on AI-generated suggestions without critically evaluating them or making independent decisions. In that case, their sense of control and belief in personal capacity may weaken, leading to a decline in human agency.
Self-Determination Theory (SDT): The Role of Motivation and Environment
Self-Determination Theory (Deci & Ryan, 2012) identifies three basic psychological needs that support intrinsic motivation: autonomy, competence, and relatedness. It is not just about what students do, but why they do it. When these needs are satisfied, learners thrive. When they’re obstructed, motivation suffers.
For example, AI can support students in decision-making and personalized learning. In this context, AI can positively influence students’ intrinsic motivation by offering meaningful choices, adapting to their preferences, and helping them experience success through tailored challenges. However, when AI systems limit students’ autonomy by making decisions on their behalf or offering overly scripted learning paths, students may feel disconnected and over-directed, undermining their self-determination.
Causal Agency Theory (CAT): Goal-Directed Action
Causal Agency Theory (Shogren et al., 2015) explains how individuals, especially SWD, become causal agents. Causal agents refer to individuals who act intentionally, volitionally, and strategically to achieve their self-defined goals. It centers on action rather than motivation or belief.
For instance, AI can support students in goal-setting and task management. In this context, AI can positively influence students’ ability to act intentionally and strategically, especially when it scaffolds the planning and execution of complex tasks. However, when AI dictates goals or automates every step of the learning process without student input, it can reduce students’ opportunities to engage in volitional decision-making, thereby hindering the development of causal agency.
Summary: How These Theories Differ
Understanding the distinct contributions of each theory is essential for leveraging AI tools that affirm agency, rather than diminish it. Below is a concise comparison:
| Theory | Core Focus | Key Concepts | Unique Perspective |
| Human Agency | Belief in one’s own power | Forethought, self-efficacy, self-reflectiveness | Emphasizes cognitive beliefs about control |
| Self-Determination | Motivation through need fulfillment | Autonomy, competence, relatedness | Emphasizes motivational conditions and environmental supports |
| Causal Agency | Intentional, goal-driven action | Volitional & agentic actions, action-control beliefs | Emphasizes one’s action-based self-determination |
Why does this matter now?
As AI shares control over planning, problem-solving, and communication, the lines of agency are becoming blurred—what some scholars now refer to as Hybrid Agency. While this may open exciting possibilities, it also introduces risk: Will Machine Agency replace Human Agency? Who holds the final say? For SWD, the stakes are especially high. Without clear frameworks, AI could inadvertently disempower the students it aims to support.
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