Language plays a central role in learning. Thought, understanding, and knowledge formation are shaped through communication with others and through internal dialogue. Sociocultural theory explains learning as a process that begins between people and becomes internal to the individual, meaning that reasoning first develops in shared interaction before becoming part of personal thinking. This perspective positions classroom talk as a core cognitive process rather than a simple method of participation.

Thinking is closely connected to communication and social activity. Cognition is embedded in dialogue, culture, and shared meaning rather than confined to the individual mind. Speaking, listening, questioning, and explaining therefore shape how learners construct understanding. Talk is not merely the expression of completed thought; it is part of the thinking process itself. Participation in dialogue allows learners to test ideas, challenge misconceptions, and refine meaning through interaction.

High-quality classroom talk supports pupils to articulate ideas, consolidate understanding, and extend vocabulary. These outcomes align with wider evidence on effective learning, where oral language development, metacognition, reading comprehension, feedback, and collaboration show strong impact on attainment. Structured dialogue is therefore a central feature of effective teaching across subjects, including computing.

Dialogic teaching treats classroom talk as primarily cognitive. Sustained interaction, reasoning-focused questioning, and the productive use of incorrect answers enable deeper understanding and higher-order thinking. The emphasis shifts from brief responses toward explanation, justification, and argument. This approach values pupil voice and aims to open space for learners to express and develop their thinking through structured exchange.

Different forms of talk shape learning in distinct ways. Disputational talk centres on assertion and challenge. Cumulative talk builds shared statements without critique. Exploratory talk combines critical engagement with constructive reasoning and is the most powerful for learning. Exploratory dialogue makes reasoning visible while maintaining intellectual rigour, enabling pupils to co-construct understanding rather than repeat information.

Effective questioning supports this process. Questions should prompt reasoning, connect to prior knowledge, and require elaborated responses using subject vocabulary. Encouraging full-sentence answers and precise language strengthens both conceptual understanding and communication. These practices influence future participation in education, employment, and civic life, where structured explanation and reasoning remain essential.

Dialogic learning requires structure as well as openness. Evidence indicates that minimally guided instruction is less effective for novice learners and can lead to misconceptions or fragmented knowledge. Strong instructional guidance and secure subject knowledge provide the foundation on which meaningful dialogue can occur. Explicit teaching and dialogic interaction therefore operate together rather than in opposition.

Artificial intelligence introduces new possibilities and risks within this landscape. Generative AI can support resource creation, lesson planning, feedback, revision activities, administration, and personalised learning. These uses may reduce workload and allow teachers to focus on teaching and pupil interaction. Benefits are currently clearer in teacher-facing applications than in direct pupil use, where evidence remains limited and risks require careful management.

Risks include misinformation, bias, safeguarding concerns, intellectual property issues, and potential weakening of the teacher–pupil relationship. Technology should enhance rather than replace human interaction in education. Responsible deployment requires attention to data protection, transparency, and accountability, alongside clear policies for safe use.

School experience reflects this mixed picture. Issues such as plagiarism, exposure to misinformation, and inappropriate content have been reported, while some settings report few difficulties. This variation reinforces the need for informed, critical engagement rather than simple acceptance or rejection of AI tools.

A foundational understanding of AI is becoming essential for all young people. While some learners will pursue deeper technical study, everyone requires knowledge to navigate an AI-driven world and to engage with ethical questions surrounding technology. Curriculum development therefore involves three connected dimensions: understanding how AI works, using AI in meaningful ways, and considering its wider implications for society and ethics.

Rapid technological change increases the importance of digital literacy, media literacy, and critical thinking. Computing education provides a natural home for these developments, including learning about data, bias, and responsible technology use.

Debate continues about the future of programming in an age of AI-generated code. Even if automation reduces routine coding, conceptual understanding remains necessary to evaluate systems, guide design, and interpret outcomes. Programming education therefore shifts toward reasoning, abstraction, and problem solving rather than simple code production. Classroom dialogue again becomes central, enabling explanation, justification, and critique.

Interaction with AI also reshapes the nature of dialogue in learning. Conversations that once occurred only between humans may now involve machine partners. This raises questions about how AI-mediated talk influences reasoning, independence, and understanding. The educational value of dialogue depends on maintaining critical thinking, social interaction, and shared meaning rather than passive acceptance of generated responses.

Several implications follow for mentoring and teacher development. Structured classroom talk should remain a priority, with deliberate design of questioning, explanation, and discussion. Dialogue must sit alongside strong subject guidance to prevent misconception. AI should be used critically and ethically to support teaching rather than replace it. All pupils require AI literacy, while human relationships and communication remain central to learning.

Language forms the bridge between sociocultural learning theory and contemporary AI-influenced education. Thought develops through dialogue, understanding grows through shared reasoning, and responsible technology use must strengthen these human processes. The cultivation of meaningful classroom talk therefore remains fundamental to effective teaching in computing and beyond.

Based on the 10th Roehampton Computing Education lecture, AI, talk and ethics, 3 December 2025