Skip to content

Artificial Intelligence in Literacy Education: Use Cases in Years 3–6

Sep 2026 · 27 references

Abstract

Abstract As Generative Artificial Intelligence (GenAI) becomes more integrated into teaching and learning in schools, it offers powerful opportunities to enhance and transform literacy instruction. This chapter presents use cases based on the professional practices and reflections of the two authors, who teach in a primary school in Australia. This chapter is structured around six key areas where GenAI-powered tools are having an impact on the authors’ practices: enabling differentiation, facilitating personalisation, guiding the decomposition of tasks, building feedback loops, deepening critical literacy and supporting the inquiry process. This chapter aims to provide practical examples drawn from Australian primary classrooms, illustrating how teachers can use GenAI to scaffold student thinking, support metacognition and foster critical engagement with texts. This chapter also touches on the ethical dimensions of GenAI use in education, including considerations about data privacy, bias and the importance of developing students’ AI literacy. The use cases demonstrate how GenAI can be employed as an augmentative tool that complements teacher expertise and decision-making rather than replacing it. While the practices described are exploratory, they offer a foundation for research and pedagogical innovation.

View source

Similar papers

#artificial intelligence Conference Open access Apr 2020

ECCOLA - a Method for Implementing Ethically Aligned AI Systems

The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.

Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson · 64 citations · ⚡6
#computer vision Review Apr 2024

AI-powered Code Review with LLMs: Early Results

The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.

Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al. · 62 citations · ⚡3
#computer vision Open access Mar 2024

LLM-based agents for automating the enhancement of user story quality: An early report

The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.

Zheying Zhang, M. Rayhan, Tomas Herda et al. · 48 citations · ⚡4
#computer vision Review Mar 2024

System for systematic literature review using multiple AI agents: Concept and an empirical evaluation

This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.

Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al. · 44 citations · ⚡2
#computer vision Feb 2024

Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis

The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.

Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al. · 41 citations

Related blog posts

GPT-Lab Sep 17, 2026

Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering

AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.