For many people, AI is a brand new thing! But the reality is that “artificial intelligence” has been around as a concept and as a reality for a very long time. It’s important to learn a brief history and context so we can better appreciate what the 21st century is offering in terms of generative AI.

Take students all the way back to Alan Turing with this eminently readable (and full of links to go down many, many rabbit holes): Britannica’s History of Artificial Intelligence entry.

This paper, Alan Turing and the Development of Artificial
Intelligence1, does a great job breaking down Turing’s 1950 article in Mind and connecting his concepts with the trajectory of AI. It’s a bit more advanced, so here are some suggestions for using this paper with students:

  • Don’t be intimidated by the mathematical theory; point students towards “2.1. Structure of the paper” to begin understanding Turing’s theories regarding the question, “Can machines think?” The objective is not to learn to become a programmer (unless you are a Computer Science major!); the goal is to have students appreciate that these conversations start in the 1940s/50s and are explicitly tied to the evolution of AI.
  • This paper can be a launch pad for questions about the language we use regarding this type of technology. For example, is “artificial intelligence” the best term?
  • Turing identified three approaches to creating a thinking machine: 1) AI by programming, 2) AI by ab initio machine learning and 3) AI using logic, probabilities, learning and background knowledge. That is, one can: create a machine that processes information like an adult mind; create a machine that is like a child and can “learn” through discipline; or, finally, create a machine that uses “unemotional” (5) logic language to reason. Which parts of these sound like AI that we’ve encountered recently?

This article from the Guardian, “Weizenbaum’s nightmares: how the inventor of the first chatbot turned against AI,” is an easier-to-read exploration of MIT professor Joseph Weizenbaum and the Eliza Effect.

Sample assignments:

  • Low-stakes journaling
    • Sample prompt: “In an informal written response of 250-350 words, please share your thoughts about Alan Turing’s driving research question, ‘Can machines think?’ What is your first gut reaction? After reading more about Turing’s response to the question, do you feel differently? Do you think his argument is persuasive?”
  • Class discussion
    • Raise a question similar to the prompt above; perhaps related to Weizenbaum’s concerns and/or the Eliza Effect. Ask students to write down their responses. Pair them up or create groups of 3-4. Invite them to craft a response to the question(s) based on all of their individual responses. Have each group report out and discuss the totality of responses.
  • Short reflection paper based on the philosophical questions raised
    • Invite students to write a letter to a person from the past, explaining to them how people of today are still grappling with the philosophical questions raised by both Turing and Weizenbaum regarding sentience, “thinking,” what it is to be human, and the fragility of humans in the wake of a human “dupe” in the form of a chatbot.
  1. Muggleton, Stephen. Alan Turing and the development of Artificial Intelligence. Volume 27, Issue 1. 1 Feb. 2014. https://doi.org/10.3233/AIC-130579 ↩︎