From Human Choices to Machine Intelligence: A Reflection on AI and Digital Learning

1. Moral Machine Reflection

The Moral Machine activity was one of the most thought-provoking activities I have experienced. Every scenario forced me to choose between two difficult options where saving one group meant sacrificing another. I realized that there is no perfect answer because every decision involves ethical values and emotions.

While answering the questions, many thoughts came into my mind. In most situations, I preferred to save children, young people, pregnant women, and athletes because I felt they had a longer future ahead of them and could contribute more to society. When I had to choose between a younger person and an elderly person, I often chose the younger person because I thought that the older person had already lived most of their life. Similarly, when the choice was between humans and animals, I usually chose to save the human lives because I believed that human life should be given greater priority.

My results also show that I generally preferred: 

Most Saved Character (Female Jogger)

Most Killed Character (Elderly Woman)

• Saving more lives instead of fewer lives.
• Saving younger people rather than older people.
• Saving fit and healthy people over less fit people.
• Saving people with higher social responsibility, such as doctors or pregnant women, over those with lower social value in the given scenarios.
• Saving humans instead of animals.

However, after completing the activity, I began to question my own decisions. I realized that my choices were influenced by my personal beliefs and assumptions about age, responsibility, and the value of life. Someone else with different experiences or cultural values might make completely different choices. This made me understand that ethical decisions are highly subjective.

HERE IS PDF OF SOME PHOTOS

Learning Outcomes

This activity taught me that developing AI is not only a technical challenge but also an ethical one. If an AI system has to make life-and-death decisions, whose values should it follow? The Moral Machine experiment showed me that machines cannot decide what is morally correct on their own because they learn from human data, and humans themselves often disagree about what is right. Therefore, fairness, transparency, and human responsibility are essential when designing AI systems.

Overall, the Moral Machine activity changed the way I think about artificial intelligence. It made me realize that behind every AI decision there are human values, and creating ethical AI is much more difficult than simply writing computer code.

Deep Dive into the Three Presentation Parts

Part 1: Decentring the Subject and the Nature of Hypertext

This section defines hypertext as a system of linked digital data including images, text, and sound stored in the cloud and accessed via a web browser Unlike traditional books, which Prof. Barad describes as "dead text" because they do not react to user interaction (like zooming or clicking), hypertext is living and linked

• Decentring: Drawing on Silvio Gaggi’s theories, the presentation argues that digital pedagogy makes the "Subject" defined as the core content, the teacher, and the taught unstable, fragmented, and decentred
• Loss of Authorship: In the computer era, traditional notions of individual authorship are often lost as readers become decentred through interactive digital networks
• Pedagogical Model: A strong digital foundation requires Content Management Systems (like Google Drive) and Learning Management Systems (like Google Classroom) to organize and deliver content effectively

Part 2: Practical Strategies for Digital Language Teaching

The second part addresses the practical challenges of teaching language and literature online, such as maintaining engagement and handling network issues

• Addressing Network Issues: To solve the problem of audio breaking due to poor connectivity, the presentation suggests using auto-transcripts and live captions (tools like Tactic or Scribble) so students can read what was spoken during the session
• The Glass Board: Prof. Barad introduces the "Glass Board" a transparent LED-lit panel that allows teachers to maintain eye contact with students while performing "board work" (like writing grammar formulas or drawing stick figures) during live or recorded sessions
• Collaborative Learning: The use of Google Docs and Spreadsheets is highlighted as a way to engage students in real-time. For example, students can collaboratively describe images, write dialogues, or practice active/passive voice in a shared digital workspace

Part 3: Generative Literature and Digital Assessment

The final part focuses on the shift toward generative literature texts produced by algorithms and Artificial Intelligence rather than human authors

• The Machine as Author: Modern AI can write poetry so convincingly that it is often difficult for humans to distinguish it from human-authored work
• Big Data in Literature: The presentation mentions using Corpus Linguistics (such as the "Click" project for Charles Dickens) to study literature through a "big data lens"
• Digital Portfolios: Prof. Barad advocates for a shift in assessment. Instead of traditional "exit exams," students should create digital portfolios where they curate and archive their work (blogs, videos, and websites) to demonstrate their academic journey and digital literacy

For a deeper dive into these concepts, you can watch the full video recording of the International Faculty Development Programme session below

• Barad, Dilip. “Pedagogical Shift from Text to Hypertext: Language & Literature to the Digital Natives.” Dilip Barad | Teacher Blog, 18 Sept. 2021,https://blog.dilipbarad.com/2021/09/pedagogical-shift-from-text-to.html
• Moral Machinehttps://www.moralmachine.net/