ACM SAC 2027: Artificial Intelligence for Education Track

 The 42nd ACM/SIGAPP Symposium On Applied Computing

During the past four decades, the ACM Symposium on Applied Computing has been amongst the major gathering forums for applied computer engineers, scientists, and application developers worldwide. SAC 2027 is sponsored by the ACM Special Interest Group on Applied Computing (SIGAPP). The aim of this track is to bring together researchers and practitioners to discuss advances, challenges, and future directions of artificial intelligence for education. 

 Track Description

Artificial Intelligence (AI) has demonstrated significant potential in addressing a wide range of challenges in education. AI technologies can personalize learning experiences, sequence instructional activities, provide individualized feedback, predict learning outcomes, identify students at risk, and support evidence-based decision-making for educators and institutions. More recently, advances in Generative AI, including large language models (LLMs), have introduced new opportunities for teaching and learning. 

Despite these benefits, the application of AI in education has faced several challenges and criticisms. Many AI systems overlook essential learning processes such as motivation, emotion, collaboration, and (meta)cognition. Educational technologies are often developed without sufficient involvement of educators, learners, and other stakeholders, limiting their pedagogical relevance and practical adoption. Furthermore, concerns remain regarding the transparency, reliability, privacy, and ethical implications of AI-driven systems. The widespread use of black-box models, including modern generative AI systems, raises important questions about explainability, trustworthiness, bias, and responsible use in educational settings. At the same time, the increasing integration of AI into schools, universities, and lifelong learning environments requires a deeper understanding of how these technologies can effectively support human teaching and learning processes.

Given the growing importance of AI in society and supporting education and the existing challenges in their applications, this technical track focuses on AI for education.

 Submissions

The AI for education track accepts regular (full) papers and posters. Selected papers, not accepted as full papers, may be accepted as poster papers. These will be presented at a poster session during the Symposium and will be published as extended abstracts in the Proceedings. Additionally, graduate students are invited to submit research abstracts to the Student Research Competition. 

Accepted papers in all categories will be published in the ACM SAC 2027 proceedings and published in the ACM digital library, indexed by Thomson ISI Web of Knowledge and Scopus.

 Important Dates

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