Beyond Chatbots: Reimagining Classroom Dialogue with AI

Author: Denis Avetisyan

A new report explores how artificial intelligence can enhance—not replace—meaningful interaction in education.

This review synthesizes insights from a recent workshop on integrating AI tools to support equitable learning, emphasizing scaffolding, explainability, and multimodal analysis of classroom dialogue.

While artificial intelligence promises transformative advances in education, realizing its potential hinges on thoughtfully balancing technological innovation with core principles of human learning and equitable access. This paper reports findings from an international workshop, “Report from Workshop on Dialogue alongside Artificial Intelligence,” convened to examine the intersection of AI and educational dialogue, specifically addressing how AI can best augment—rather than replace—meaningful classroom interaction. Participants identified key conditions under which AI can foster better dialogic teaching and learning, while also raising critical questions about the potential for displacement of human educational work. Can we proactively shape the integration of AI to prioritize both learning outcomes and the enduring value of human agency in education?

Adaptive Learning: The Promise and Peril of AI Integration

Traditional educational models often struggle to accommodate individual learning needs and diverse styles. This one-size-fits-all approach can hinder engagement and achievement. AI offers a path to augment teaching through adaptive support, personalized platforms, and automated assessment. However, ethical considerations and the preservation of human interaction are paramount. Algorithms must promote equity, transparency, and data privacy, empowering teachers rather than replacing them. The future of learning resides in the convergence of artificial intelligence and sound pedagogy.

Equitable Systems: Designing for Access and Trust

Effective AI integration requires a commitment to equity and access. AI tools can personalize learning, but without careful consideration, they risk exacerbating existing disparities. Targeted interventions and resource allocation are essential to ensure all students benefit. Transparency through Explainable AI (XAI) is critical for fostering trust and understanding algorithmic reasoning. Accountability mechanisms, including regular audits, are vital to address biases and ensure ethical, effective use, supporting student progress.

Validation Through Collaboration: A Rigorous Approach

Developing effective AI tools for education necessitates collaboration between researchers, developers, and educators. This co-design process ensures alignment with pedagogical principles and user needs. Rigorous testing beyond initial development, including cross-cultural validation, is crucial to ensure appropriateness and effectiveness across diverse contexts. Longitudinal impact studies are needed to assess long-term effects on student development, including cognitive skills and well-being.

Human Agency: Centering Learners in an AI-Driven World

The prevalence of AI in education demands careful consideration of its impact on fundamental learning objectives. While AI offers benefits in personalization and access, its implementation should safeguard the development of crucial human skills, particularly empathy and critical thinking. Prioritizing human agency is essential; learners must remain active participants, empowered to make informed choices and critically evaluate information. AI should function as an assistive tool, preserving learner autonomy. Beyond individual skills, AI can enhance civic participation by facilitating access to diverse perspectives, but requires cultivating media literacy and critical evaluation skills.

Frameworks for Innovation: Building the Future of AI in Education

Advancing AI in education requires shared coding frameworks to facilitate collaboration and accelerate the creation of resources. Standardized interfaces and protocols promote interoperability and reduce redundancy. Robust professional development initiatives are essential, training teachers not only in the technical aspects of AI but also in pedagogical strategies. Current learning and cognition frameworks must be updated to fully account for the evolving role of AI, guiding future innovation in the field.

The workshop’s emphasis on explainable AI and scaffolding mirrors a fundamental tenet of robust algorithm design. Donald Knuth observed, “Premature optimization is the root of all evil.” This resonates deeply with the report’s call for transparency; just as a poorly understood algorithm can introduce subtle errors, an opaque AI system hinders effective pedagogical integration. The pursuit of ‘working’ solutions, without a firm grasp of the underlying mechanisms – the invariants, if you will – ultimately undermines the potential for truly augmenting classroom dialogue and achieving equitable learning outcomes. The focus must remain on provable, understandable systems, not simply those that appear to function.

What’s Next?

The proposition of integrating artificial intelligence into educational settings, while superficially appealing, immediately presents a challenge of reproducibility. Current multimodal analyses, intended to scaffold dialogue, rely on complex algorithms. Unless these algorithms are demonstrably deterministic – yielding identical outputs given identical inputs – the claim of ‘improvement’ remains empirically weak. A statistically significant result derived from a non-deterministic system is, at best, a snapshot in time, and carries little weight when considering long-term educational impact.

The emphasis on ‘equity’ is commendable, yet risks becoming a fashionable metric obscuring deeper issues. True educational equity doesn’t simply mean equal access to an AI tutor; it demands a provable guarantee that the underlying algorithms are free from inherent biases, and that their ‘scaffolding’ does not inadvertently reinforce existing inequalities. The burden of proof lies not in demonstrating correlation, but in establishing causal relationships with mathematical certainty.

Future work must therefore move beyond descriptive studies of ‘human-AI interaction’ and focus on the formal verification of these systems. Until the principles governing these algorithms can be stated with the precision of a theorem, and their effects predicted with absolute confidence, the integration of AI into the classroom will remain a fascinating, but ultimately unreliable, experiment.

Original article: https://arxiv.org/pdf/2511.05625.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/