Pseudo-agency: students may appear engaged without understanding the epistemic purpose of their actions¶

Claim · All claims
Evidence · 1 study · theoretical r3 · q1
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q1 i? Student agency is not automatic and must be intentionally cultivated; pseudo-agency occurs when students follow procedures or use AI outputs without questioning validity or articulating why their actions matter. → Wood 2026

Evidence¶

Wood 2026¶

Wood, D., & Moss, S. (2026). Cultivating Agency and Responsible AI Use Through the AI-ARC Framework. Journal of Instructional Design and Technology, 1(1), 8–17. https://doi.org/10.65201/CJHS2205

q1 · i? · theoretical · r3

The paper reports, citing González-Howard et al. (2024), an example in which student participation met the surface requirements of inquiry but failed to reflect genuine decision-making or conceptual understanding. The reviewed study is attributed, not reanalyzed, by this paper.

"students simply followed a checklist provided by the teacher without articulating why their chosen procedures mattered for answering the research question."

Discussion¶

Related Claims¶

• Responsible AI use and student agency are mutually reinforcing capacities — related
• Distributed epistemic agency emerges as learners attribute authority to AI outputs and shift responsibility for knowledge validation, though some engage dialogically — possibly the same claim (merge candidate)
• In interviews with 25 engineering students at one Swedish university, students described GenAI as acceptable when it stimulates reflection and directly copying its outputs as cheating, and one used its fallibility as a prompt to double-check — related
• In unguided GenAI collaboration, ethnic minority preparatory students’ strategy talk co-occurred with passive sharing and uncritical acceptance, forming a plan-and-paste loop — possibly the same claim (merge candidate)
• Students exercise ethical agency in GenAI use through human-AI collaboration strategies, personal ethical frameworks, strategic calibration of use, and resistance to over-reliance — related
• Agency erosion appeared in 88% of student essays, most often as erosion of independent effort — related
• Teachers are positioned as moral gatekeepers of AI use without adequate structural support — related
• Reviewed literature reports risks of overreliance, AI errors in complex tasks, and students' uncritical acceptance of AI-generated outputs — related
• Marzano's taxonomy chosen over Bloom's because it accommodates three knowledge types and positions metacognition and ethics — related