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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. [ABSTRACT FROM AUTHOR]

Formative assessment is essential for improving teaching and learning, and AI and visualization techniques provide great potential for its design and delivery. Using NLP, cognitive diagnostic and visualization techniques designed to analyse and present students' monthly exam data, we developed an AI‐enabled visual report tool comprising six modules and conducted an empirical study of its effectiveness in a high school biology classroom. A total of 125 students in a ninth‐grade biology course were assigned to a treatment group (n = 63) receiving AI‐enabled visual reports as the intervention and a control group (n = 62) receiving overall oral feedback from the teacher. We present the main statistical results of the within‐subjects design and the between‐subjects design respectively, to better capture the main findings. Repeated measures ANOVA revealed a significant interaction effect of intervention and time on learning achievement, and the paired‐sample Wilcoxon test indicated that the treatment group had experienced increasing learning anxiety (Cohen's d = 0.203, p = 0.046) and self‐efficacy (Cohen's d = 1.793, p = 0.000) over time. Moreover, we conducted a series of non‐parametric tests to compare the effects of AI‐enabled visual reports and teacher feedback, but found no significant differences except for an increased self‐efficacy (Cohen's d = 0.312, p = 0.046). Additionally, we had the students in the treatment group rate their favourable modules in the AI‐enabled visual report and provide evaluative feedback. The study results provide important insights into the design and implementation of effective formative assessment supported by artificial AI and visualization techniques. Practitioner notesWhat is already known about this topic Formative assessment is essential for improving teaching and learning.Traditional formative assessment tools lack accurate data‐oriented assessment and usability.AI and visualization techniques have great potential for formative assessment.What this paper adds This study designs and implements an AI‐enabled visual report tool that generates data‐driven, user‐friendly reports.The AI‐enabled visual report can not only enhance students' learning achievement and self‐regulated learning over time but also increase their test anxiety.The AI‐enabled visual report has a comparable effect with teacher feedback but leads to increased self‐efficacy.Implications for practice and/or policy We recommend using the AI‐enabled visual report in large‐size classes for its overall positive effects on both learning achievement and self‐regulated learning.We recommend using the AI‐enabled visual report over teacher feedback for its capacity to enhance students' self‐efficacy.We recommend prioritizing the modules of Performance Ranking, Personal Mastery and Knowledge Alert when designing the AI‐enabled visual report. [ABSTRACT FROM AUTHOR]