Education has always been about creating opportunities for people to develop new skills, competencies, and productive attitudes. Education goes beyond simply communicating knowledge and aims to teach individuals analytical and critical thinking, social skills, and human values, thus preparing them for society.

Rapid advances in Artificial Intelligence (AI) have created opportunities not only for personalized and immersive experiences but also opportunities for ad hoc learning by engaging with cutting-edge technology continually, extending classroom borders, from engaging in real-time conversations with large language models (LLMs) to creating expressive artifacts such as digital images with generative AI or physically interacting with the environment for a more embodied learning.

As a result, we now need new approaches and measurements to harness this potential and ensure that we can safely and responsibly cope with a world in transition. It is now evident that in order to move forward in terms of AIED practice, we need to consider AI Literacy and AI policy. Furthermore, in the past new technological advancements typically led to broadening social gaps. We envision that the triad of AIED, AI Literacy, and Fair, and Ethical AI will play a fundamental role in this world in transition and be the drivers for shaping meaningful changes in pedagogical practice, educational policies, and regulations.

This year’s theme for AIED is "AIED for a World in Transition". The conference aims to explore how AI can be used to enhance the learning experiences of students and teachers alike when disruptive technologies are turning education upside down. The conference seeks to stimulate discussion of how AI can shape education for all sectors, how to advance the science and engineering of AI-assisted learning systems, and how to promote broad adoption. Engaging with the various stakeholders – researchers, educational practitioners, businesses, policy makers, as well as teachers and students – the conference will set a wider agenda on how novel research ideas can meet practical needs to build effective intelligent human-technology ecosystems that support learning in new, complex, and unknown scenarios.

AIED 2024 will be the 25th edition of the International AIED Society. The AIED Society organises the AIED conference and is aimed at advancing science and engineering of intelligent human-technology ecosystems that support learning. The conference will be the latest of a longstanding series of international conferences, known for high quality and innovative research on AI-assisted systems and cognitive science approaches for educational computing applications. AIED is ranked A in CORE (top 16% of all 783 ranked venues), the well-known ranking of computer science conferences. AIED 2024 solicits empirical and theoretical papers particularly (but not exclusively) in the following lines of research and application:


AI-assisted and Interactive Technologies in an Educational Context: Data-driven processing techniques (educational data mining, deep learning, machine learning); Knowledge representation and reasoning; Generative AI; Semantic web technologies; Multi-agent architectures; Tangible interfaces, Wearables; Natural language processing and speech technologies; Virtual and augmented reality.

Modeling and Representation: Models of learners, including open learner models; facilitators, tasks and problem-solving processes; Models of groups and communities for learning; Modeling motivation, metacognition, and affective aspects of learning; Ontological modeling; Computational thinking and model-building; Representing and analyzing activity flow and discourse during learning; Representing and modeling psychomotor learning.

Models of Teaching and Learning: AI-assisted tutoring and scaffolding; Motivational diagnosis and feedback; Learner engagement; Interactive pedagogical agents and learning companions; Agents that promote metacognition, motivation and positive affect; Adaptive question-answering and dialogue, Data-driven modeling (educational data mining, deep learning, machine learning,...); Learning analytics and teaching support, Learning with simulations; Explainability of models for teaching and learning.

Learning Contexts and Informal Learning: Game-based learning; Collaborative and group learning; Social networks; Inquiry learning; Social dimensions of learning; Communities of practice; Ubiquitous learning environments; Learning through construction and making; Learning grid; Lifelong learning; Learning in informal settings (museum, workplace, etc.); Learning in the physical space; Learning of motor skills.

Evaluation: Studies on human learning, cognition, affect, motivation, engagement, and attitudes; Design and formative studies of AIED systems; Evaluation techniques relying on computational analyses.

Innovative Applications: Domain-specific learning applications (e.g. language, science, engineering, mathematics, medicine, military, industry, sports and more); Scaling up and large-scale deployment of AIED systems.

Equity and Inclusion in Education: Socio-economic, gender, and racial issues; AI-assisted techniques to support students from under-resourced schools and communities; Sponsorship, scientific validity, participant’s rights and responsibilities, data collection, management and dissemination.

Ethics of AI in Education: Explainability, transparency, accountability, and responsible AIED; learner consent and opt out; surveillance and privacy; the impact of AIED on teachers, learners and classrooms; teacher empowerment and student agency; the community’s responsibility for commercial applications; AIED ethical frameworks and principles for application.

AI Literacy: Skills and knowledge that enable individuals to understand, use, and critically evaluate AI; Definitions of AI literacy; Learning to use AI; Developing a basic understanding of how AI works; Learning to communicate and collaborate with AI; learning to live with AI, Understanding limitations and problems of AI.

AIED for Development: Focuses on leveraging AI technology to address and improve various aspects of development in societies and education, particularly in low and middle-income countries; AIED Divide; AIED Unplugged; Low-cost solutions; Low-tech solutions; Frugal Innovation; Jugaad Innovation.

Explore Design, Use, and Evaluation of Human-AI Hybrid Systems for Learning: Research that explores the potential of human-AI interaction in educational contexts; Systems and approaches in which educational stakeholders and AI tools build upon each other’s complementary strengths to achieve educational outcomes and/or improve mutually.

Online Learning Spaces: Massive open online courses; Remote learning in k-12 schools; Synchronous and asynchronous learning; Mobile learning; Active learning in virtual settings; Video-based learning; Mixed reality and learning.

Human-AI Partnership: Shared decision making between systems and users that promote agency and improve learning.

AI in Ed for Theory: Using bottom-up and top-down approaches to analyze data in order to inform learning theories and gain better understanding of the socio-cognitive nature of learning.