Human-AI collaboration in education: continual learning systems as adaptive instructional partners
Ghazal Barari, Alyssa Ann DeNaro Dewees, Nicki BarariPurpose
Continual learning models offer a transformative approach to artificial intelligence (AI) in education by enabling systems to incrementally adapt to new tasks and data while preserving previously acquired knowledge. This stands in contrast to static AI systems, which are trained once on fixed datasets and cannot evolve after deployment. In dynamic educational environments where student needs, curricular goals and teaching strategies shift frequently, this adaptability is essential. This article explores how continual learning systems can support human-AI collaboration by functioning as intelligent instructional partners rather than replacements for educators.
Design/methodology/approach
We propose two conceptual applications: (1) intelligent tutoring systems that evolve with each cohort while supporting instructor oversight and (2) AI-assisted curriculum co-design tools that analyze longitudinal learning patterns to guide course refinement. These concepts are examined through two case studies situated within engineering education.
Findings
When embedded in human-in-the-loop frameworks, continual learning systems have the potential to function as adaptive, trustworthy educational technologies. The proposed intelligent tutoring system is designed to support personalized feedback without forgetting prior instructional logic. The proposed curriculum co-design tool is intended to track outcome performance trends across terms and provide evidence-informed insights for curriculum review.
Research limitations/implications
As conceptual prototypes, the proposed systems prioritize architectural innovation over immediate empirical validation. This intentional abstraction allows for the reimagining of AI's role in education but limits current measurement of efficacy or scalability. The reliance on continual learning assumes future-ready data infrastructures and institutional willingness to co-evolve with adaptive systems, conditions not yet universally met. These limitations are catalytic, not constraining: they invite interdisciplinary research into explainability, governance and human-AI co-design. Realizing the full potential of these models will require iterative development, simulation-rich testing environments and strategic alignment with evolving standards for ethical, transparent AI in education.
Practical implications
Embedding continual learning into educational AI systems enables sustained instructional adaptability, helping institutions respond to evolving learner needs, curriculum shifts and assessment strategies without retraining models each term. The proposed intelligent tutoring system supports faculty in delivering personalized, context-aware feedback, while the curriculum co-design tool empowers programs to make data-informed revisions grounded in longitudinal trends. These tools can reduce faculty workload, enhance instructional agility and promote evidence-based teaching practices. Institutions that invest in such systems will be better positioned to foster responsive, scalable and learner-centered education, particularly in dynamic disciplines like engineering where content mastery evolves over time.
Social implications
By developing human–AI partnerships based on continual learning, this framework supports more equitable, transparent, and responsive educational ecosystems. These systems can help reduce achievement gaps by personalizing support based on evolving learner needs, while preserving pedagogical consistency across cohorts.
Originality/value
This work introduces a novel approach to educational AI that emphasizes co-evolution with educators. By embedding continual learning into instructional and curricular tools, this work proposes a foundation for sustainable, transparent and relevant AI-enhanced learning ecosystems. Future directions include implementation within virtual learning environments and broader programmatic assessment.