Month: January 2025

Artificial intelligence is a cornerstone of educational technology, making it crucial to critically evaluate its scope and limitations. Understanding the impact of machine intelligence on both our own and our students’ learning and educational development is vital. In this post, we explore the differences in responses between humans and ChatGPT to gain insights and reflect on its implications.

ARTIFICIAL INTELLIGENCE – click to read more.

While both my responses and ChatGPT’s answers offer an informative explanation of AI-related topics, my responses are more personalized and reflect a deeper connection to the subjects. I try to humanize the technical explanations, emphasizing not just the facts but also offering my perspective on what makes these concepts important in practical, real-world contexts. For instance, when discussing Alan Turing, I highlight his contribution to challenging the boundary between human and machine thought, which mirrors the broader implications for AI today. I bring an emotional and philosophical dimension to the discussion, which goes beyond merely summarizing his work.

In contrast, ChatGPT’s responses are more neutral and concise. It excels in providing factual summaries that are direct and clear. However, ChatGPT’s responses can sometimes lack a human touch in terms of the emotional depth or personal insights that I attempt to bring out, such as the deeper significance of concepts like creativity and ethical reasoning in human intelligence versus machine intelligence.

When addressing more technical aspects, such as machine languages versus human languages, ChatGPT is highly efficient at providing structured, clear contrasts. Yet, I try to incorporate broader societal and emotional implications—how programming languages prioritize logical clarity, while human languages enable rich, empathetic communication.

Regarding machine learning versus human learning, ChatGPT’s version is very factual and precise, whereas I focus on how human learning integrates intuition, culture, and personal motivations, offering a more holistic understanding. In summary, ChatGPT’s responses are technically sound but lack the nuance, reflection, and personal insights that a human like me might bring to these topics.

References:

Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433-460. https://doi.org/10.1093/mind/LIX.236.433

McCarthy, J. (2007). What is artificial intelligence? Stanford University. https://www-formal.stanford.edu/jmc/whatisai/whatisai.html

UBS. (n.d.). Meet the Nobel laureates in economics: Do we understand human behaviour? UBS. https://www.ubs.com/global/en/our-firm/people-and-culture/nobel-laureates-in-economics/understanding-human-behaviour.html

BBC News. (2016, January 24). AI pioneer Marvin Minsky dies aged 88. BBC News. https://www.bbc.com/news/technology-35301023

Hao, K. (2020, December 4). We read the paper that forced Timnit Gebru out of Google. Here’s what it says. MIT Technology Review. https://www.technologyreview.com/2020/12/04/1013359/timnit-gebru-google-ai-diversity-paper/

Harris, J. (2018, September 27). Languages vs. programming languages. Medium. https://medium.com/@jackharris_/languages-vs-programming-languages-35b582b4d6e9

Chollet, F. (2019). On the measure of intelligence. arXiv. https://arxiv.org/abs/1902.04197

Heilweil, R. (2020, March 5). Why algorithms can be racist and sexist. A computer can make a decision faster. That doesn’t make it fair. Vox. https://www.vox.com/recode/2020/3/5/21165938/algorithm-bias-ai-discrimination-racism-sexism

Buolamwini, J. (2019). Artificial intelligence has a problem with gender and racial bias. Here’s how to solve it. Wired. https://www.wired.com/story/artificial-intelligence-problem-with-bias/

Social Cognitive Theory, as outlined by Bandura (2011), deeply resonates with me when I reflect on how social media has influenced my life. The idea of reciprocal determinism—where our thoughts, behaviours, and environment interact and shape one another—perfectly captures the role social media plays in shaping my perspectives and habits.

For me, LinkedIn has been both empowering and challenging. On one hand, it has connected me with incredible people around the world who share similar passions and goals. These connections inspire me and open new opportunities for growth. But on the other hand, I’ve experienced anxiety and self-doubt when I see others achieving milestones like certifications, new roles, or workshop completions. It’s easy to feel like I’m falling behind. Over time, I’ve learned to honour my own pace and see these moments as inspiration rather than competition. Orey’s (2002) discussion on the role of memory and information processing aligns with this realization—what we repeatedly expose ourselves to on social media becomes ingrained in how we perceive ourselves. With more mindfulness, I now approach these experiences with self-compassion, though it’s still a work in progress.

Image Source: Instagram feed

Social media also profoundly influences how behaviours are modelled. Bandura’s (2001) concept of observational learning resonates here, as we constantly witness influencers showcasing curated lives. Personally, I’ve noticed how this affects my expectations for success or perfection. I’ve also seen the negative impacts, such as how idealized content can erode confidence or self-efficacy when comparisons creep in. Orey’s emphasis on cognitive processing further explains how this influx of information can overwhelm and create biases in how we evaluate our own worth.

Reflecting on all of this, SCT has given me a framework to better understand my relationship with social media. It has made me more conscious of how I process and respond to the flood of information I encounter daily. This perspective has encouraged me to prioritize authenticity in my online interactions and embrace a healthier, more balanced relationship with social media.

References:

Bandura, A. (2011). Chapter 17: Social cognitive theory. In P. A. M. van Lange, A. W. Kruglanski, & E. T. Higgins (Eds.), Handbook of Social Psychological Theories (pp. 349-373). London: Sage.

Bandura, A. (2001). Social Cognitive Theory of Mass Communication. Media Psychology, 3(3), 265-299.

Orey, M. (2002). Information Processing. In M. Orey (Ed.), Emerging perspectives on learning, teaching, and technology (pp. 25-34). A Global Text.

What Makes Technology Truly Usable? A Deep Dive into Usability

Usability is all about making technology easy and enjoyable for people to use. It’s more than just how quickly someone can figure out a system or how few mistakes they make—it’s about the whole experience. From reading Issa and Isaias (2015), I’ve come to see usability as a journey. It’s about creating a bridge between what technology can do and what people actually need it to do. The key? Put people at the centre of the process. Listen to their feedback, make adjustments, and keep improving. Usability is never a one-and-done deal; it’s an ongoing evolution.

But here’s the catch: in education, usability has to go further. Learning tools can’t just be easy to use—they have to help people learn effectively. Educational usability means designing tools that support learning by managing cognitive load, offering step-by-step guidance, and providing clear feedback. For example, a great tool doesn’t overwhelm users with too much information at once; instead, it guides them along in a way that’s engaging and meaningful. Accessibility is also critical, ensuring that learners of all abilities can use the tool. So, while general usability is about simplicity and efficiency, educational usability is about creating an environment where people can grow and succeed.

When Usability Goes Wrong: Lessons from Woolgar

Woolgar’s work is a bit of a reality check. I would admit it was not an easy read but it was indeed cynically humorous and engaging. It shows how even with the best intentions, things can go wrong. He uses the term “configuring users,” which is basically about the assumptions designers make about who will use a system and how. Sometimes, those assumptions are way off, and that’s where the problems start.

Take one example Woolgar discusses: designers assumed that all users were tech-savvy. They built an interface that required users to know shortcuts and technical terms. But when real users tried it, they struggled because they didn’t have that knowledge. It’s like handing someone a manual car without teaching them how to drive stick shift. Another example is how designers simplified tasks for usability trials. They broke tasks into neat little steps to make testing easier, but real-life tasks are messier and interconnected. The system worked fine in a lab, but when people tried to use it in the real world, it didn’t meet their needs. These examples are a reminder: you can’t design in a bubble. Real-world testing with real users is essential.

Additionally, while I was reading both texts, I couldn’t help but think back to my own experience with my work team during the transition from existing system to a new system launched recently. It was a tough change, and I remember asking why the program admin staff—the ones using the system daily—weren’t consulted more. Apparently, new system did consult users, but they focused on higher-level graduate program advisors rather than the admin staff who dealt with the system every day. To make things worse, they didn’t even include students as users, even though they’re a key group. That experience really drove home the lesson for me: systems need to be designed for the actual users, not just a select group. The user shouldn’t have to adapt to the system—the system should adapt to them.

Two Ways to Think About Usability

This brings me to two very different ways people think about usability. One sees usability as a technical problem to solve with metrics and testing. It’s about speed, accuracy, and reducing errors. The other sees usability as a human-centered journey, focusing on how technology fits into people’s lives and adapts to their needs. It’s less about numbers and more about a holistic and compassionate approach to understanding one’s experiences.

Both approaches have their strengths. The first is great for ensuring a system is functional and efficient. But the second makes sure it’s meaningful and adaptable. The best designs, I think, strike a balance between these two views. They deliver solid performances while also feeling natural and intuitive to use.

Why It Matters

After much exploration, one thing is clear: usability isn’t just a box to check off. It’s about creating experiences that truly meet people’s needs. For educational tools, that means going beyond the basics to support learning in a way that’s engaging and effective. Woolgar’s examples highlight how easy it is to make wrong assumptions, and my own experience with Workday reinforces the importance of consulting the right users. The two views on usability remind us that good design needs both efficiency and a holistic and compassionate approach to understanding one’s experiences. At the end of the day, the best systems meet people where they are and help them get where they want to go. That’s what makes technology truly usable.

References:
1. Issa, T., & Isaias, P. (2015) Usability and human computer interaction (HCI).In Sustainable
 Design (pp. 19-35). Springer.

2. Woolgar, S. (1990). Configuring the user: The case of usability trials. The Sociological
Review, 38(1, Suppl.), S58-S99.