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Hal Abelson

Hal Abelson

· Professor of Computer Science and Electrical Engineering

Massachusetts Institute of Technology · Electrical Engineering and Computer Science

Active 1937–2025

h-index23
Citations4.4k
Papers8417 last 5y
Funding$1.1M

Academic metrics are sourced from OpenAlex and public funding records; values may differ from Google Scholar.

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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

Selected publications

  • From computational thinking to computational action

    Communications of the ACM · 2019-02-21 · 191 citations

    articleSenior author

    Envisioning computing education that both teaches and empowers.

    PublisherDOI

  • The Effects on Secondary School Students of Applying Experiential Learning to the Conversational AI Learning Curriculum

    The International Review of Research in Open and Distributed Learning · 2022 · 57 citations

    The purpose of this study was to design a curriculum of artificial intelligence (AI) application for secondary schools. The learning objective of the curriculum was to allow students to learn the application of conversational AI on a block-based programming platform. Moreover, the empirical study actually implemented the curriculum in the formal learning of a secondary school for a period of six weeks. The study evaluated the learning performance of students who were taught with the cycle of exp…

    PublisherOA PDFDOI

  • Behavioral-pattern exploration and development of an instructional tool for young children to learn AI

    Computers and Education Artificial Intelligence · 2021 · 47 citations

    This study aimed at developing an instructional tool for the artificial intelligence education of young students, and used learning analytics to identify the sequential learning behavioral patterns of students during the process of learning with the instructional tool. The instructional experiment took 9 weeks. The first stage of the course was 5 weeks spent on individual learning of MIT App Inventor and Personal Image Classifier. The second stage was 4 weeks spent on cooperative learning to mak…

    PublisherDOI

  • Is It Possible for Young Students to Learn the AI-STEAM Application with Experiential Learning?

    Sustainability · 2021 · 39 citations

    This study attempted to evaluate the learning effectiveness of using the MIT App Inventor platform and its Personal Image Classifier (PIC) tool in the interdisciplinary application. The instructional design was focused on applying PIC in the integration of STEAM (i.e., Science, Technology, Engineering, Art, and Mathematics) interdisciplinary learning, so as to provide sustainable and suitable teaching content based on the experiential learning theory for 7th grader students. Accordingly, the sus…

    PublisherOA PDFDOI

  • Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlation

    arXiv (Cornell University) · 2022-12-09 · 24 citations

    preprintOpen access

    We investigate whether three types of post hoc model explanations--feature attribution, concept activation, and training point ranking--are effective for detecting a model's reliance on spurious signals in the training data. Specifically, we consider the scenario where the spurious signal to be detected is unknown, at test-time, to the user of the explanation method. We design an empirical methodology that uses semi-synthetic datasets along with pre-specified spurious artifacts to obtain models…

    PublisherOA PDFDOI

Recent grants

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

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