Advancing equity and inclusion in educational practices with AI-powered educational decision support systems (AI-EDSS)

A key goal of educational institutions around the world is to provide inclusive, equitable quality education and lifelong learning opportunities for all learners. Achieving this requires contextualized approaches to accommodate diverse global values and promote learning opportunities that best meet the needs and goals of all learners as individuals and members of different communities. Advances in learning analytics (LA), natural language processes (NLP), and artificial intelligence (AI), especially generative AI technologies, offer potential to aid educational decision making by supporting analytic insights and personalized recommendations. However, these technologies also raise serious risks for reinforcing or exacerbating existing inequalities; these dangers arise from multiple factors including biases represented in training datasets, the technologies' abilities to take autonomous decisions, and processes for tool development that do not centre the needs and concerns of historically marginalized groups. To ensure that Educational Decision Support Systems (EDSS), particularly AI-powered ones, are equipped to promote equity, they must be created and evaluated holistically, considering their potential for both targeted and systemic impacts on all learners, especially members of historically marginalized groups. Adopting a socio-technical and cultural perspective is crucial for designing, deploying, and evaluating AI-EDSS that truly advance educational equity and inclusion. This editorial introduces the contributions of five papers for the special section on advancing equity and inclusion in educational practices with AI-EDSS. These papers focus on (i) a review of biases in large language models (LLMs) applications offers practical guidelines for their evaluation to promote educational equity, (ii) techniques to mitigate disparities across countries and languages in LLMs representation of educationally relevant knowledge, (iii) implementing equitable and intersectionality-aware machine learning applications in education, (iv) introducing a LA dashboard that aims to promote institutional equality, diversity, and inclusion, and (v) vulnerable student digital well-being in AI-EDSS. Together, these contributions underscore the importance of an interdisciplinary approach in developing and utilizing AI-EDSS to not only foster a more inclusive and equitable educational landscape worldwide but also reveal a critical need for a broader contextualization of equity that incorporates the socio-technical questions of what kinds of decisions AI is being used to support, for what purposes, and whose goals are prioritized in this process.