karin schmidlin

IP 2: Artificial Intelligence

Alan Turing


Prompt: In 50 words, tell me who Alan Turing was?

Alan Turing was a British mathematician and computer scientist who is widely considered to be the father of modern computing. During WW2, he worked for the British government on code-breaking efforts. After the war, he continued to make important contributions to the field of computer science and artificial intelligence. He committed suicide in 1954.


Alan Turing's 1936 Universal Machine, which could accomplish several tasks using a programming language, influenced modern computers (Gleick 2011, p.212). He eventually amended his question of whether machines can think (Turing, 1950) to the "Turing Test", which determines if a computer is intelligent by its ability to fool a human.


Prompt: In 50 words, tell me who John McCarthy was?

John McCarthy was an American computer scientist and cognitive scientist, who is widely considered as one of the founders of the field of artificial intelligence. He was responsible for coining the term "Artificial Intelligence" in 1955 and made important contributions to the development of the LISP programming language and the development of AI programming languages.


In 1956 in a workshop at Dartmouth University, John McCarthy coined the term “Artificial Intelligence.” His idea was that every aspect of learning and intelligence could be accurately described so that a computer could replicate it (Russel, 2019, p.4). While instrumental for AI, it turned out to be significantly more difficult than expected.

John McCarthy


In 1957, economist Herbert Simon predicted that computers would beat humans in chess within ten years (Davenport et al., 2019). This prediction was 30 years off. Simon's AI research on information processing and decision-making is well-regarded. He characterised intelligence as "bounded rationality," limited by information and computational resources available for decision-making.


Prompt: In 50 words, tell me who Herbert Simon was?

Herbert Simon was an American economist, political scientist, and cognitive psychologist who received the Nobel Memorial Prize in Economics in 1978. He was known for his work on decision-making and organizational behavior and was a pioneer in the field of artificial intelligence. He was a professor at Carnegie Mellon University and passed away in 2001.

Herbert Simon


Marvin Minsky co-organized the 1956 Dartmouth workshop that launched artificial intelligence (Russel 2019, p. 4). His 1967 prediction of "the challenge of producing artificial intelligence being solved" (Davenport et al. 2019) was decades off. He considered the mind, natural or artificial, as made up of smaller chunks, he called 'agents' (Brand, 1987).


Prompt: In 50 words, tell me who Marvin Minsky was?

Marvin Minsky was an American computer scientist, a pioneer in the field of artificial intelligence and cognitive psychology. He co-founded the MIT Media Lab and the MIT Computer Science and Artificial Intelligence Laboratory. He was known for his work on AI and cognitive psychology and is considered one of the founders of the field of AI.

Marvin Minsky

Timnit Gebru


Timnit Gebru is a widely recognized practitioner of ethics in AI, presently working at the Distributed AI institute that she established after her highly public departure from Google. Her main criticism centres on the company’s lack of accountability for the discriminatory implications of AI as well as the power dynamics of AI generally.


Prompt: In 50 words, tell me who Timnit Gebru is?

Gebru is a computer scientist known for her work on AI ethics. She is a co-founder of the Black in AI group and a former researcher at Google. Gebru is also a co-author of the influential paper "Datasheets for Datasets" and has been recognized for her contributions to the field of AI ethics and diversity in tech.

People in Artificial Intelligence

People of AI

Machine vs Human Language 💬

Both computer and human languages are collections of symbols and rules designed to facilitate communication. While a computer language has stricter rules and a smaller vocabulary and aims to provide clear instructions to a machine, human languages have evolved over generations, are based on a human's physical features, can change depending on the context, and include a plethora of dialects and slang (Harris, 2018). Its primary use is to convey information, emotions, and ideas from one person to another. The difference between machine - and human languages becomes relevant in AI tools that apply speech recognition to deliver solutions for humans.

Human intelligence can be defined by attributes such as consciousness, creativity, and self-awareness, whereas machine intelligence is characterized by intelligence simulated based on its capacity to process massive quantities of data in order to execute complicated tasks fast. ChatGPT, for example, is a model that predicts the most probable sequences of words and does not have awareness like a human. Some AI researchers advocate for the development of human-like intelligent systems (Cholet, 2019), whereas others argue for the advancement of non-human intelligent systems (Boden, 2016), which adhere to a transhumanist vision of a future mind that is not biologically rooted (Coeckelbergh, 2020).

Machine vs Human Intelligence 💡

Machine vs Human Learning 🎓

Humans learn from experience, repetition, social interactions, reinforcement, and feedback, which allows for behaviour modification and information prioritization. Human learning has been defined as the formation of an abstract internal model of the external world (Dehaene, 2020). Machines, on the other hand, have a more difficult time distinguishing between levels of information. They are trained on enormous volumes of data and use algorithms to find patterns in order to produce the most promising predictions.Both humans and computers are biassed, but whereas humans may use logic and reasoning to contextualise their learning, machine-learning systems are opaque black boxes that frequently result in harmful discrimination (Buolamwini, 2019).

A Turing Test 🤖

The written responses above are a combination of annotated sections from the suggested readings, library and online searches to enhance my comprehension and lastly, my own notes and books on the subject from my vast collection. Despite the fact that ChatGPT doesn’t reference the sources applied, and is, in essence, a recycling engine (Seife, 2023) that regurgitates widely-available information it has been trained on, it is a useful tool to augment my learning. Its written output may resemble mine. However, if the purpose of an assignment such as this IP 2, is for the student to deeply engage with the material and construct their own knowledge, then a far deeper level of learning might be at play. ChatGPT fills the role of a private tutor, breaking down complex concepts into more digestible language and drawing my attention to issues I may have overlooked.  It is not always possible to see evidence of the learning experience in the produced output of an assignment. The final point is an extremely important consideration when evaluating a student’s learning journey in the context of AI tools such as ChatGPT. When considering the role that AI technologies play in my teaching and learning practise, I intend to look at things through this lens.

Remaining Questions

Q1: How might we add a tool like ChatGPT to the Learning Analytics suite to assess a student's learning journey holistically?

Q2: Instead of measuring what students produce, how might we apply ChatGPT to measure what students understand?


Boden, M. A. (2016). AI: Its nature and future. Oxford University Press.

Buolamwini, J. (2019). Artificial Intelligence Has a Problem With Gender and Racial Bias. https://time.com/5520558/artificial-intelligence-racial-gender-bias/

Brand, S. (1987). The media lab: Inventing the future at MIT. Viking Penguin, New York, NY.

Coeckelbergh, M. (2020). AI ethics. Mit Press.

Chollet, F. (2019). On the measure of intelligence.https://doi.org/10.48550/arxiv.1911.01547

Davenport, T. H., Brynjolfsson, E., McAfee, A., & Wilson, H. J. (2019). Artificial intelligence: The insights you need from Harvard business review. Harvard Business Press.

Dehaene, S. (2020). How we learn: The new science of education and the brain. Penguin UK.

Gleick, J. (2011). The information: A history, a theory, a flood (1st ed.). Pantheon Books.

Hao, K. (2020, December 4). We read the paper that forced Timnit Gebru out of Google. Here’s what it says Links to an external site.. MIT Technology Review.

Harris, A. (2018, October 31). Languages vs. programming languages. Links to an external site.Medium.

Illing, S. (Host). 2023, January 9). Is ethical AI possible? [Audio podcast episode]. In The Gray Area. Vox.https://www.stitcher.com/show/vox-conversations/episode/is-ethical-ai-possible-210621040

Russell, S. (2019). Human compatible: Artificial intelligence and the problem of control. Penguin.

Seife C. (2023, January 31). A.I. like ChatGPT is revealing the insidious disease at the heart of our scientific process. Slate. https://slate.com/technology/2023/01/ai-chatgpt-scientific-literature-peer-review.html

Turing, A. M. (2012). Computing machinery and intelligence (1950). The Essential Turing: The Ideas That Gave Birth to the Computer Age, 433-464.