The idea for Logo turned sixty this year. So did I.
Logo, a programming language designed for children, began in 1966 at Bolt, Beranek and Newman. Its inventors were Seymour Papert, Wallace Feurzeig, Daniel Bobrow and Cynthia Solomon (Solomon et al., 2020). I knew Papert and I knew Logo. I had never heard of Solomon. That made me want to find out more about her.
Solomon co-designed Logo and stayed with it for decades. She was vice-president of research and development at Logo Computer Systems when Apple Logo was released and later directed the Atari Cambridge Research Laboratory. She is the first author of History of Logo and tells her own account of that history through LogoThings. Papert is still the name I associate with Logo. Learning about Solomon made me think about Watters’ (2020) argument that this field forgets too easily.
Logo was designed as more than a programming language. Children could use it to explore mathematical ideas and create their own projects. Its development drew on Piaget’s constructivism and Minsky’s artificial intelligence research, with a commitment to giving children a powerful programming environment (Solomon et al., 2020). Papert and Solomon (1971) argued for children using computers to create and explore rather than computers simply delivering instruction.
Kafai and Morales-Navarro (2023) revisit Papert and Solomon’s 1971 memo in the context of AI and machine learning. Their argument that learners should build AI applications rather than only use finished ones carries the same emphasis on the learner doing the intellectual work.
I taught software development as generative AI became widely available. I saw students produce working code they could not always explain or debug. That is what makes Solomon’s work interesting to me sixty years later. How do we give students powerful tools without taking away the thinking they need to learn?
References
Kafai, Y. B., & Morales-Navarro, L. (2023). Twenty constructionist things to do with artificial intelligence and machine learning. Proceedings of FabLearn/Constructionism 2023. https://arxiv.org/abs/2402.06775
Papert, S., & Solomon, C. (1971). Twenty things to do with a computer (AI Memo No. 248). MIT Artificial Intelligence Laboratory. https://dspace.mit.edu/handle/1721.1/5836
Solomon, C., Harvey, B., Kahn, K., Lieberman, H., Miller, M. L., Minsky, M., Papert, A., & Silverman, B. (2020). History of Logo. Proceedings of the ACM on Programming Languages, 4(HOPL), Article 79, 1–66. https://doi.org/10.1145/3386329
Watters, A. (2020, November 11). What happens when ed-tech forgets? Some thoughts on rehabilitating reputations. Hack Education. https://hackeducation.com/2020/11/11/forgetting
AI Use Plan and Transparency Statement
I will use ChatGPT to help identify possible people for this assignment and review my writing for clarity. I will treat information and sources generated by AI as starting points rather than evidence. I will open the sources myself and check that they exist, that publication details are correct, and that they support the claims I make. Earlier in this course, an AI response gave me a DOI that did not match the source when I checked it, so I will not use AI-generated citations without verification. I will make the final decisions about the person, sources, argument and wording used in the post.
Reflection
AI helped me explore several possible people before I chose Cynthia Solomon. It produced information that needed checking. One AI recommendation was Safiya Noble, but some of the supporting links were incomplete, and I also realized that identifying her view of learning would require me to infer more than I was comfortable claiming. Solomon was a better fit because her work gave me direct evidence for both her contribution to the field and the view of learning behind it.
Checking the details also caught smaller problems. An AI response described one source as a sixty-year history of Logo even though it was published in 2020 as a fifty-year history. I also removed tracking information from links before using them. These checks reminded me that even when an AI response sounds convincing, I still need to decide whether the information is accurate and whether it supports my argument. In that sense the tool could help me, but I still had to do the thinking.
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