About

Hal Abelson is the Class of 1922 Professor at MIT, specializing in Computer Science and Artificial Intelligence + Decision-making. His research focuses on the development of systems that interact with the external world through perception, communication, and action, while also learning, making decisions, and adapting to changing environments. His work combines intellectual traditions from computer science and electrical engineering to analyze and synthesize intelligent systems. As a prominent figure in the field, Professor Abelson contributes to advancing understanding in artificial intelligence, machine learning, and educational technology. His expertise encompasses a broad range of topics within AI and decision-making, emphasizing the development of systems that can learn and adapt in complex environments.

Research topics

• Computer Science
• Artificial Intelligence
• Mathematics education
• Psychology
• Political Science
• Computer Security
• Machine Learning
• Mathematics
• Pedagogy
• Internet privacy
• Law
• Engineering
• Human–computer interaction
• Business

Selected publications

• Supporting AI Literacy Teaching Through the Development of Assessments for Classroom Use

  Proceedings of the AAAI Conference on Artificial Intelligence · 2025-04-11 · 1 citations

  articleOpen accessSenior author

  Initial discussion of AI literacy assessment has focused on competency frameworks and learning standards rather than materials for classroom use. Responsible AI for Computational Action (RAICA), a constructionist AI curriculum for middle and high school students, includes assessment materials to support teachers with the evaluation of student AI literacy competencies in their classrooms. These materials include exit tickets used as formative assessments at the end of each lesson and both teacher and student-facing rubrics. After beta-testing a module of the curriculum with nine teachers and 282 students, we reviewed teacher usage data and feedback as well as student responses. The review process surfaced a number of improvements to the materials to better align them with classroom teaching practice. These included clarifying language and adding visual scaffolds. We present the assessment materials and iterative design process used to bridge the gap between the theoretical AI literacy competencies and their practical implementation in classrooms.

  PublisherOA PDFDOI

• Advancing Research on Equitable AI Education Through a Focus on Implementation: Insights from a Middle School Computer Vision Module Beta-Test

  Proceedings of the AAAI Conference on Artificial Intelligence · 2025-04-11

  articleOpen access

  Part of a university initiative supporting responsible AI for social empowerment and education, the project-based RAICA (Responsible AI for Computational Action) curriculum supports middle/high school learners and novice AI literacy teachers use AI creatively for good. This paper offers a rare example of design-based implementation research (DBIR) in AI education across widely varied contexts, provides fine grain implementation data that contributes to a foundation for evaluating effectiveness and expanding access. We present a novel approach to analyzing fidelity of implementation data from RAICA’s computer vision module beta-test. Twelve educators working with ~282 students across nine pilot sites in four countries used a bespoke fidelity of implementation data collection tool (pre-made comment prompts in a Google Docs version of the teacher guide) to provide 236 qualitative responses about AI literacy and responsible design activities, plus 111 ordinal ratings of embedded teacher supports. Analyses revealed that while the curriculum was generally implemented as designed, educators frequently made modifications. Although most changes produced practical insights for improved curriculum design, others helped the design team anticipate and prevent changes that could obscure learning objectives and hinder outcomes. We discuss the pedagogical, design, and research implications of these findings for effective AI teaching/learning in diverse settings.

  PublisherOA PDFDOI

• A Picture Is Worth a Thousand Words: Co-designing Text-to-Image Generation Learning Materials for K-12 with Educators

  Proceedings of the AAAI Conference on Artificial Intelligence · 2024-03-24 · 18 citations

  articleOpen access

  Text-to-image generation (TTIG) technologies are Artificial Intelligence (AI) algorithms that use natural language algorithms in combination with visual generative algorithms. TTIG tools have gained popularity in recent months, garnering interest from non-AI experts, including educators and K-12 students. While they have exciting creative potential when used by K-12 learners and educators for creative learning, they are also accompanied by serious ethical implications, such as data privacy, spreading misinformation, and algorithmic bias. Given the potential learning applications, social implications, and ethical concerns, we designed 6-hour learning materials to teach K-12 teachers from diverse subject expertise about the technical implementation, classroom applications, and ethical implications of TTIG algorithms. We piloted the learning materials titled “Demystify text-to-image generative tools for K-12 educators" with 30 teachers across two workshops with the goal of preparing them to teach about and use TTIG tools in their classrooms. We found that teachers demonstrated a technical, applied and ethical understanding of TTIG algorithms and successfully designed prototypes of teaching materials for their classrooms.

  PublisherOA PDFDOI

• Generative AI and K-12 Education: An MIT Perspective

  2024-03-27 · 26 citations

  articleOpen access

  In November of 2022, a Silicon Valley company launched an invention that could complete students’ homework for them. Available only to subscribers at first, by the spring of 2023 OpenAI’s ChatGPT-3.5 was available to millions of students. As of January 2023, anyone with . . .

  PublisherOA PDFDOI

• Opportunities, Issues, and Challenges for Generative AI in Fostering Equitable Pathways in Computing Education

  2024-08-28 · 2 citations

  preprintOpen access
  PublisherOA PDFDOI

• "Data comes from the real world": A Constructionist Approach to Mainstreaming K12 Data Science Education

  2024-12-02 · 5 citations

  articleOpen access

  SIGCSE Virtual 2024, December 5–8, 2024, Virtual Event, NC, USA

  PublisherDOI

• Day of AI: Innovating Pedagogical Practices to Bring AI Literacy to Classrooms at Scale

  Lecture notes on data engineering and communications technologies · 2023-01-01 · 6 citations

  book-chapter
  PublisherDOI

• UNDERSTANDING TEACHER PERSPECTIVES AND EXPERIENCES AFTER DEPLOYMENT OF AI LITERACY CURRICULUM IN MIDDLE-SCHOOL CLASSROOMS

  ICERI proceedings · 2023-11-01 · 14 citations

  articleOpen access

  Artificial Intelligence (AI) and its associated applications are ubiquitous in today's world, making it imperative that students and their teachers understand how it works and the ramifications arising from its usage. In this study, we investigate the experiences of seven teachers following their implementation of modules from the MIT RAICA (Responsible AI for Computational Action) curriculum. Through semistructured interviews, we investigated their instructional strategies as they engaged with the AI curriculum in their classroom, how their teaching and learning beliefs about AI evolved with the curriculum as well as how those beliefs impacted their implementation of the curriculum. Our analysis suggests that the AI modules not only expanded our teachers' knowledge in the field, but also prompted them to recognize its daily applications and their ethical and societal implications, so that they could better engage with the content they deliver to students. Teachers were able to leverage their own interdisciplinary backgrounds to creatively introduce foundational AI topics to students to maximize engagement and playful learning. Our teachers advocated their need for better external support when navigating technological resources, additional time for preparation given the novelty of the curriculum, more flexibility within curriculum timelines, and additional accommodations for students of determination. Our findings provide valuable insights for enhancing future iterations of AI literacy curricula and teacher professional development (PD) resources.

  PublisherOA PDFDOI

• THE EFFECT OF COMPUTATIONAL ACTION ON STUDENTS’ COMPUTATIONAL IDENTITY AND SELF-EFFICACY

  EDULEARN proceedings · 2023-07-01 · 1 citations

  articleSenior author

  Appears in: EDULEARN23 Proceedings Publication year: 2023Pages: 8404-8412ISBN: 978-84-09-52151-7ISSN: 2340-1117doi: 10.21125/edulearn.2023.2183Conference name: 15th International Conference on Education and New Learning TechnologiesDates: 3-5 July, 2023Location: Palma, Spain

  PublisherDOI

• “How Can I Code A.I. Responsibly?”: The Effect of Computational Action on K-12 Students Learning and Creating Socially Responsible A.I.

  Proceedings of the AAAI Conference on Artificial Intelligence · 2023-06-26 · 7 citations

  articleOpen accessSenior author

  Teaching young people about artificial intelligence (A.I.) is recognized globally as an important education effort by organizations and programs such as UNICEF, OECD, Elements of A.I., and AI4K12. A common theme among K-12 A.I. education programs is teaching how A.I. can impact society in both positive and negative ways. We present an effective tool that teaches young people about the societal impact of A.I. that goes one step further: empowering K-12 students to use tools and frameworks to create socially responsible A.I. The computational action process is a curriculum and toolkit that gives students the lessons and tools to evaluate positive and negative impacts of A.I. and consider how they can create beneficial solutions that involve A.I. and computing technology. In a human-subject research study, 101 U.S. and international students between ages 9 and 18 participated in a one-day workshop to learn and practice the computational action process. Pre-post questionnaires measured on the Likert scale students’ perception of A.I. in society and students' desire to use A.I. in their projects. Analysis of the results shows that students who identified as female agreed more strongly with having a concern about the impacts of A.I. than those who identified as male. Students also wrote open-ended responses to questions about what socially responsible technology means to them pre- and post-study. Analysis shows that post-intervention, students were more aware of ethical considerations and what tools they can use to code A.I. responsibly. In addition, students engaged actively with tools in the computational action toolkit, specifically the novel impact matrix, to describe the positive and negative impacts of A.I. technologies like facial recognition. Students demonstrated breadth and depth of discussion of various A.I. technologies' far-reaching positive and negative impacts. These promising results indicate that the computational action process can be a helpful addition to A.I. education programs in furnishing tools for students to analyze the effects of A.I. on society and plan how they can create and use socially responsible A.I.

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: WAVES - A STEM-Powered Youth News Network for the Nation

  NSF · $1.1M · 2016–2019

Frequent coauthors

• Guillermo J. Rozas

  Massachusetts Institute of Technology

  23 shared

• Gerald Jay Sussman

  23 shared

• G. Brooks

  21 shared

• Norman I. Adams

  Palo Alto Research Center

  21 shared

• D. H. Bartley

  21 shared

• Kent M. Pitman

  Harvard University

  20 shared

• R. Kent Dybvig

  Cisco Systems (United States)

  20 shared

• Chris Hanson

  20 shared

Education

• Ph.D., Computer Science

  Massachusetts Institute of Technology

  1972

• B.S., Mathematics

  Harvard University

  1967

Awards & honors

• 2025-26 EECS Faculty Award Roundup