AI hype is driving interest in longer term impacts in all sectors of society. Education is not immune. For academics, administrators, and staff, the rapid pace of development of generative AI, coupled with industry driven declarations of how education will change, requires a thoughtful, nuanced, research-informed response. 

GRAILE is launching a 12-month speaker series: Sensemaking Lectures. Each month, a speaker will frame a theme of discussion and conversation. Following the lecture, discussions will be held reviewing important literature and exploring implications in educational settings. These discussions will happen asynchronously, with periodic Zoom discussions, with a global community of interested researchers.


2023 Sessions

June 2, 4pm EST: Register here.

Adrienne Williams

Adrienne started organizing in 2018 while working as a junior high teacher for a tech owned and operated charter school. She expanded her organizing in 2020 after her work as an Amazon delivery driver during the pandemic afforded her the ability to see that many of the same issues that caused her to leave the charter schools were happening at Amazon as well. Since then she has worked both on the ground and behind the scenes with activists, politicians, researchers, and everyday people to enact positive change in the tech, labor, and education industries by exposing and educating the public on how these industries harm and ways in which that harm can be reversed. Adrienne began working as a Research Fellow at the DAIR Institute in 2022, in hopes that her unique lived experience of working within and organizing against these industries aids in pushing towards a more equitable society.

July 19

A New Science of Learning with AI

As we come to understand the affordances and limitations of generative AI, it is time to flip the narrative away from “How will AI impact education?” to “What are new and effective ways of teaching and learning with AI?”. In this presentation I will explore how AI can support innovative pedagogy. We know from research into the science of learning that effective learning requires teaching spaced over time, interleaving of topics, practice in retrieving items from memory, elaboration of ideas, concrete examples, and combining words and images. Other evidence-based teaching practices include setting clear goals, giving timely feedback, supporting group learning, and promoting critical thinking. Generative AI systems can be configured to enable effective learning while addressing their tendency to “hallucinate” false information. Roles for generative AI include: Possibility Engine (AI generates alternative ways of expressing an idea), Socratic Opponent (to develop an argument), Collaboration Coach (to assist group learning), Exploratorium (to investigate and interpret data), and Personal Tutor. Future research into generative AI for education should be based on a new science of learning with AI – to include developing generative AI systems that have long term memory, set explicit goals, and explain their reasoning. Rather than seeing AI as a challenge to traditional education, we can exploit it to prepare students for a future where AI is a tool for creativity and active learning, to be operated with great care and awareness of its limitations.


Mike Sharples

Emeritus Professor of Educational Technology

Institute of Educational Technology

Mike Sharples is Emeritus Professor of Educational Technology at The Open University, UK. His expertise involves human-centred design and evaluation of new technologies and environments for learning. He holds a PhD from the Department of Artificial Intelligence at the University of Edinburgh on the topic of “Cognition, Computers and Creative Writing”. He is an Associate Editor of the International Journal of Artificial Intelligence in Education. He founded the Innovating Pedagogy report series and is author of over 300 papers in the areas of educational technology, learning sciences, science education, human-centred design of personal technologies, artificial intelligence and cognitive science. His recent books are Practical Pedagogy: 40 New Ways to Teach and Learn, and Story Machines: How Computers Have Become Creative Writers, both published by Routledge.


Detecting AI Hype

Emily Bender

Emily has been a member of the faculty at the University of Washington since 2003. She is a Professor in the Department of Linguistics and the faculty director of the CLMS program and the director of the Computational Linguistics Laboratory. For 2019-2022, Emily was honored to be the Howard and Frances Nostrand Endowed Professor. I am an Adjunct Professor in both the School of Computer Science and Engineering and the Information School at UW, and a member of the Tech Policy LabValue Sensitive Design Lab, and RAISE.


Vania Dimitrova

Vania Dimitrova leads research activity on human-centred artificial intelligence which builds intelligent systems that help people make sense of data, take decisions in complex settings, expand their knowledge, learn from experience, and develop self-regulation skills. Her research explores the use of data and knowledge models to get insights into user-generated content, understand users and influence behaviour, capture knowledge and support information exploration. Her research is conducted in cross-disciplinary collaboration with researchers from Medicine and Health, Engineering, Social Science, Education and Psychology, and actively involving end users. She is currently President of the International AI in Education Society and Co-Director of the UKRI Centre for Doctoral Training in AI for Medical Diagnosis and Care. She was Co-Director of the Leeds Research Centre in Digital Learning and was Director of Technology Enhanced Learning Strategy at the Leeds Institute of Medical Education. She is Associate Editor of the International Journal of AI in Education, and Frontiers of AI: AI for Human Learning and Behavior Change. She was Associate Editor of IEEE Transactions on Learning Technologies (IEEE-TLT) and a member of the editorial boards for the personalisation journal (UMUAI). She chaired the premier international conference on user modelling (ACM UMAP) and key conferences in intelligent learning environments (AIED, ECTEL, ICCE), as well as a series of international workshops on key topics related to intelligent mentoring, user modelling, social systems, intelligent exploration.


Nia Dowell

Dr. Nia Nixon (née Dowell), Assistant Professor in Education UC-Irvine, is Vice President of the Society for Learning Analytics Research (SoLAR) and the Director of The Language and Learning Analytics Laboratory (LaLA-Lab). The LaLA Lab includes researchers with backgrounds in cognitive science, information, psychology, and statistics. The LaLA-Lab takes a multi-disciplinary approach that builds on theories and methods in the cognitive sciences, human-computer interaction, and computational social sciences. Dr. Dowell and her team conduct research on socio-cognitive and affective processes across a range of educational technology interaction contexts and develop computational models of these processes and their relationship to learner outcomes. Their research uses a range of artificial intelligence (AI) techniques such as computational linguistics and machine learning. Current projects focus on i) understanding differences in students’ socio-cognitive engagement patterns across gender and racial lines, ii) identifying interpersonal dynamics that characterize varying levels of creativity/innovation and sense of belonging during collaborative interactions, and ii) developing AI based interventions to promote inclusivity in digitally mediated team problem-solving environments.


Wayne Holmes

Having been involved in education throughout his life, Wayne brings a critical studies perspective to the connections between AI and education, and their ethical, human and social justice implications. His recent publications include “Artificial Intelligence in Education. Promise and Implications for Teaching and Learning.” (2019), “Ethics of AI in Education: Towards a Community-Wide Framework.” (2021),  “State of the Art and Practice in AI in Education” (2022), “The Ethics of AI in Education: Practices, Challenges and Debates” (2022), and, for UNESCO, “AI and Education: Guidance for Policy-makers.” (2021). Wayne has advised the ministries of education of Portugal and the UK, co-authored the EU’s DigComp 2.2 Annex “Citizens Interacting with AI Systems” (2022), is leading the Council of Europe’s work on AI and education, and has given invited talks on AI and education in Brazil, China, Croatia, Denmark, Germany, Greece, India, Japan, Oman, Slovenia, Spain, and the US (and online to audiences in many other countries around the world).


Soft skill development and AI

Tanja Mitrovic

Antonija Tanja Mitrović is a New Zealand computer scientist.

Mitrovic did her MSc and PhD at the University of Niš in Niš, Serbia. Before moving to the University of Canterbury in Christchurch, New Zealand and rising to the level of professor.

Mitrovic specialises in artificial intelligence methods in online-learning systems, particularly in modelling the students’ understanding based on previous questions and using the model to select future questions.

2024 Sessions


Benedict du Boulay

Benedict du Boulay is an Emeritus Professor of Artificial Intelligence in the School of Engineering and Informatics at the University of Sussex and Visiting Professor at University College London.

He has two main research areas. The first is the Psychology of Programming where his main work has been in the area of novices learning programming and the development of tools to assist that process. The second is the application of Artificial Intelligence in Education. Here he is particularly interested in issues around modelling and developing students’ metacognition and motivation.

He has co-organised various workshops on the above areas. These have included the 1st and 2nd International Workshop on Affect, Meta-Affect, Data and Learning (AMADL 2015 in Madrid, and AMADL 2016 in Zagreb) and the workshop on “Les Contes du Mariage: Should AI stay married to Ed?”, also in Madrid in 2015. He has successfully supervised 25 PhD students in the above areas.

He was President (2015-2017) and is currently Treasurer and Secretary of the International Society for Artificial Intelligence in Education and an Associate Editor of its International Journal of Artificial Intelligence in Education. He has edited/written 9 books and written some 190 papers in the areas indicated above.


Olaf Zawacki-Richter

Olaf Zawacki-Richter is a professor of educational technology at the University of Oldenburg in Germany. He is the Dean of the Faculty of Education and Social Sciences and Director of the Center for Open Education Research (COER). Olaf has over 25 years of professional experience in the field of open, distance, and digital education. He has also served as a consultant and advisor, including work for the United Nations’ International Labor Organization, the Office of Technology Assessment at the German Bundestag, and the German Science and Humanities Council (Wissenschaftsrat).

Dr. Zawacki-Richter has authored over 150 journal articles and edited several books – all published open access. He is an Associate Editor of the “Online Learning Journal” (OLJ) and a member of the editorial board of the “International Review of Research in Open and Distance Learning” (IRRODL), “Open Learning”, the “Turkish Online Journal of Distance Education” (Turkey), and the “The Journal for Higher Education Development” (Austria). In 2022 Olaf published together with Professor Insung Jung the open-access Handbook of Open, Distance, and Digital Education (ODDE) in Springer’s Major Reference Series.



May 10, 4pm EST

Generating Chaos: A Futurist Considers AI Over the Next Decade

How might recent developments in artificial intelligence transform education and society?  In this presentation we take a futurist’s perspective, analyzing current trends and hunting for signals of the ways the world might change after large language models.  We start by imagining AI progressing steadily if unevenly, and its effects ripple throughout civilization, using historical precedents and innovation diffusion theory as guides. We extrapolate ways the AI industry can grow, change, contract, and grow again. We examine potential transformations in culture, work, policy, geopolitics, and other technologies, including how they might embrace and/or resist the new toolset. The presentation concludes by offering several scenarios for how colleges and universities could change after grappling with LLMs.

Bryan Alexander

Bryan Alexander is an award–winning, internationally known futurist, researcher, writer, speaker, consultant, and teacher, working in the field of higher education’s future.

He completed his English language and literature PhD at the University of Michigan in 1997, with a dissertation on doppelgangers in Romantic-era fiction and poetry.