Beyond Task Completion: A Theoretical Integration and Framework for Guiding Students' ChatGPT Use for Learning

Synthesis: Øby (2026) develops an explanatory framework for students' ChatGPT use grounded in learning theory, integrating cognitive load theory and goal orientation (Motivation) with Self-Efficacy and task relevance as mediating constructs. The core claim is that generative AI can either support learning or enable superficial task completion depending on how it is integrated into academic work. Four perspectives — cognitive demands, motivational orientations, perceived competence, and perceived task value — shape students' decisions about when and how to rely on genAI, determining whether it functions as a scaffold supporting thinking and knowledge construction or a shortcut that bypasses essential cognitive processes.

Key Findings

• Scaffold versus shortcut: Whether generative AI supports learning or enables superficial task completion is contingent on cognitive and motivational conditions shaped within instructional contexts — not inherent to the tool.
• Cognitive load as the mechanism: ChatGPT can reduce extraneous load by structuring information and activating prior knowledge, but when outputs replace engagement with interacting elements, intrinsic load is bypassed rather than managed. Task complexity is closely tied to element interactivity.
• Motivational orientations drive use: Mastery and normative performance goals tend to support AI use for elaboration and refinement, whereas appearance performance goals increase the likelihood AI is used to minimize effort and protect perceived competence — a channeling of achievement goals through engagement with academic tasks.
• Self-Efficacy mediates: Students who believe they can succeed in demanding tasks are more likely to sustain engagement and adopt mastery-oriented strategies; attributing success to external tools rather than effort constrains efficacy growth and reinforces shortcut patterns.
• Task relevance shapes investment: When tasks are perceived as central to academic or professional development, students tolerate intrinsic load and use AI to elaborate understanding; when peripheral, efficiency concerns dominate and AI is used to reduce effort.
• AI literacy as Pedagogies and Teaching Strategies: The framework positions genAI use as an instructional variable, suggesting that educators can design learning conditions that manage cognitive load and support adaptive orientations rather than relying on reactive policy restrictions.

Implications for AI in Education

The framework reframes guidance on student AI use from procedural academic integrity policy toward learning-theory-grounded instructional design. Practitioner notes emphasise that students' ChatGPT use shifts between supporting learning and bypassing cognitive effort depending on task complexity and cognitive load, so instruction should sequence tasks with gradually increasing element interactivity to keep intrinsic load aligned with learner expertise. How generative AI is framed in course design and classroom communication influences whether students treat it as a learning support or a shortcut; explicit discussion helps students recognize when generative tools support their reasoning and when they replace it. Structuring activities into incremental steps allows students to experience manageable successes and attribute progress to their own strategies, strengthening confidence and preserving engagement with core reasoning. Framing ChatGPT as a cognitive tool for exploring ideas and refining reasoning encourages deeper engagement with disciplinary tasks, and the framework argues this mirrors established metacognitive support — a contrast to accounts of cognitive offloading as an effortless trap that produces an illusion of learning. Because achievement goals respond to contextual cues, motivational climate becomes a key lever in shaping whether AI supports deep engagement or surface-level completion.

Connected Concepts

• Cognitive Offloading
• Self-Efficacy
• Motivation
• Metacognition
• Student Engagement
• Generative AI
• Scaffolding
• Higher Education
• AI in Education

Connected Articles

• Artificial intelligence, cognitive offloading and implications for education — Cognitive offloading in the context of AI use
• Metacognitively Discordant Completion and the Aware Pass-Through of Non-Understanding in Generative AI Learning — Metacognitive discordance in genAI completion
• Think First, ChatGPT Later: Guiding Human–AI Collaboration for Learning Gains in Independent Human Creativity — Think first, ChatGPT later
• The Effortless Trap: Productive Struggle, AI, and the Illusion of Learning — The effortless trap: productive struggle and the illusion of learning

Citation

Øby, E. (2026). Beyond task completion: A theoretical integration and framework for guiding students' ChatGPT use for learning . Journal of University Teaching and Learning Practice, 23(5).