Assuring Quality Learning in a Gen AI-Integrated Future: The Role of Adaptive Capabilities

Synthesis: Lodge et al. (2026), a TEQSA-commissioned report, proposes that assuring quality learning in a gen-AI-integrated future depends on cultivating four adaptive capabilities in graduates: digital literacy, distributed cognition, hybrid metacognition, and life-long learning — all supported by agency and regulation at the centre. The report argues these durable, human-centred capabilities (built on deep disciplinary knowledge) let graduates maintain agency and use gen AI as a partner rather than becoming dependent on it. It offers five propositions of practice: make adaptive capabilities core graduate attributes, build institutional infrastructure for learning process evidence, design environments that promote them, shift toward process-focused assessment, and foster cross-institutional collaboration. This is a significant quality-assurance and policy framework for AI-mediated Australian higher education.

Key Findings

1. Four adaptive capabilities define a human-centred foundation for gen-AI-integrated learning. Digital literacy, distributed cognition, hybrid metacognition, and life-long learning — supported by agency and regulation — enable graduates to navigate complex, novel and gen-AI-integrated environments by deploying, adapting and transferring skills effectively and ethically.
2. Agency and regulation sit at the centre. The human capacity to plan, monitor, evaluate and adjust learning and decision-making (self-regulated and co-regulated learning) underpins all four capabilities, letting learners maintain autonomy and manage Cognitive Offloading in complex contexts.
3. Five propositions re-orient practice. Graduate attributes, learning-process-evidence infrastructure, environment design, process-focused assessment, and cross-institutional collaboration collectively translate the Threshold Standards into actionable mechanisms.
4. The shift is from products to processes. Assuring learning quality requires evidencing how students learn (traces, regulatory activity, gen-AI interactions) — not just final products and outcomes — because gen AI can generate many traditional assessment products.

The four adaptive capabilities

• Digital literacy — using digital tools (including gen AI) effectively, ethically and safely; extends the Australian Digital Capability Framework (DigComp-based) with a critical understanding of gen AI's principles, methods, limitations, ethics, societal impact and power structures.
• Distributed cognition — how cognitive processes and tasks are shared across people, tools, artefacts and gen AI systems: working in human–gen AI teams, using external representations and agents to extend thinking, understanding knowledge flow across distributed systems, coordinating roles and resources.
• Hybrid metacognition — the regulation of thinking and learning within any cognitive system, including human–gen AI networks: planning, monitoring, evaluating and adjusting cognitive processes, whether in the individual or distributed. Ensures learners maintain agency, intentionality and ethical awareness (evaluative judgement, deciding when to rely on or override gen AI, co-regulation, ethical reasoning).
• Life-long learning — sustaining motivation, capability and adaptability to learn continuously in evolving, uncertain, gen-AI-mediated contexts: adapting to evolving technologies, identifying knowledge gaps, transferring learning, developing a learner identity grounded in agency.

Five propositions of practice

1. Establish adaptive capabilities as core graduate attributes. Embedding the four capabilities operationalises Threshold Standards graduate-attribute requirements across disciplines, aligned with the National Skills Taxonomy and Australian Digital Capability Framework. Professional learning for educators focuses on scaffolding students' adaptive capabilities within human–gen AI networks.
2. Build institutional infrastructure for learning process evidence. Invest in systematic capacity to collect and analyse data on learning processes — traces students generate as they plan, monitor, seek feedback, revise, and interact with gen AI (time-stamped edits, resource-use patterns, problem-solving sequences, metacognitive prompts, structured self-assessments) — prioritising student Agency, privacy and transparency (e.g., open learner models).
3. Design learning environments that promote adaptive capabilities through evidence-informed pedagogical practices, including desirable difficulties and the value of productive struggle.
4. Transform pedagogical practice toward process-focused assessment. Rebalance assessment so learning-process documentation complements (not replaces) product evaluation. Learning analytics make processes visible; research (e.g., Raković et al. 2023) shows process features can explain more variance in performance than product features alone. Requires training educators and fading scaffolding as students develop self-regulated learning.
5. Foster collaborative innovation across institutions. Cross-institutional collaboration shares resources, research findings and approaches, building sector-wide capacity while prioritising human agency and educational effectiveness over technological capability.

Implications for AI in education

This report is a policy and quality-assurance contribution to the wiki's Educational Policy AI thread, connecting Higher Ed quality standards to a concrete capability framework. It aligns with the assessment-reform strand by arguing that learning processes (not just products) are the durable evidence gen AI cannot simulate — reinforcing Authentic Assessment and Assessment Validity. The framework's emphasis on agency, Self Regulated Learning and Metacognition resonates with epistemic proactivity, and its process-focused Learning Analytics recommendations connect to the wiki's learning-analytics thread. It is the companion document to earlier TEQSA assessment-reform resources.

Connected Concepts

• Higher Ed
• AI Literacy
• Metacognition
• Self Regulated Learning
• Lifelong Learning
• Distributed Cognition
• Collaborative Learning
• Adaptive Learning
• Generative AI
• Learning Analytics
• Assessment Validity
• Authentic Assessment
• Educational Policy AI
• Governance
• Ethics
• Agency

Connected Articles

• AI Assessment Scale Reform — Assessment reform for the age of AI
• GenAI Assessment Governance — GenAI assessment governance
• Enright Staff Perspectives GenAI 2026 — Staff perspectives on GenAI
• Epistemic Proactivity Math — Epistemic proactivity in AI-supported math learning

Citation

Lodge, J. M., de Barba, P., Ainscough, L., Brazil, J. R., Broadbent, J., Ebbert, D., Frankland, S., Gabriel, F., Gašević, D., Hennicke, T., Lim, L.-A., Male, S. A., Mirriahi, N., Oliveira, E. A., Pacitti, H., Raković, M., Russell, J., Taylor-Griffiths, D., & Yang, S. (2026). Assuring quality learning in a gen AI-integrated future: The role of adaptive capabilities. TEQSA, June 2026.