Reframing Memories of Academic Uncertainty With a Constructive-Developmental AI Reduces Expressed Uncertainty in STEM Learners
Year: 2026
Type: preprint
Abstract: Conversational AI can alter what people later remember, yet its effects on how students remember their own academic experiences are untested. We examined whether a large language model (LLM)’s reframing of memories of academic uncertainty changes how STEM undergraduates later describe those memories. Participants recalled five such memories and received an LLM-generated response to each: a reframe instructed to move them toward a self-authoring perspective according to Constructive-Developmental theory, or a restatement that elaborated the uncertainty. One week later, Self-Authorship participants used less tentative language when redescribing their memories, whereas restatement produced no reliable change. LLM-generated vocabulary reappeared in later descriptions of the specific memories it addressed in both conditions. The reduction in tentative language remained after novel reframe vocabulary was removed from the Session 2 descriptions. Emotional valence did not differ by condition. A brief interaction with a theory-constrained AI agent can change how learners later describe academic uncertainty.
Source: Research Square
Authors: Maximilian Ramseyer, Ayanna Thomas
Institution: Tufts University
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7225644022
Cites:
Cited by:
Related to: