This article identifies the main risks of framing questions for independent study using AI in education as Techno-solutionism, which is the belief in simple technological solutions to society’s complex problems. It uses the One Laptop per Child project (OLPC) as a case study, where despite its innovative approach, it failed due to neglecting local context. Similar pitfalls might exist in the current hype surrounding the integration of AI into education. Education interventions must consider local needs and culture, rather than the need to integrate new tech into learning.
Alongside this main argument, I've also written two other short articles on two accompanying perspectives, focusing on the limitations of Techno-solutionism, including historical research and an Eastern viewpoint on education and wisdom.
I'll present a simple fact: for most of human history, we didn't have computers, yet we were still able to produce knowledge. This highlights that technology is indeed a powerful tool, but it may not be the essence of learning.
The Eastern view of education and wisdom offers an alternative direction of learning that includes the necessity of slowing down information processing, creating mental space for inner peace, and contemplating knowledge to discern right from wrong. It's important to note that having more power and knowledge without the right direction can lead to destruction.
In this article, I delve into the challenges faced by Fulbright first-year students by sharing insights from a group project conducted in the "Community Development" course. The focus was to evaluate the resources and needs of these students. By examining existing Fulbright resources, conducting interviews with six students, and reviewing literature on first-year challenges, we aimed to define the learning problem based on the social and cultural context of Fulbright.
To address the learning challenges of Fulbright first-year students, I propose a solution: an open notebook and video course that delve into the basics of learning science and essential of note-taking as information processing strategies. This approach prioritizes human factors over technology, providing a cost-effective solution that can be applied to various educational programs. The product, developed over two years of personal study on cognitive psychology and note-taking strategies. It has been shared at workshops and online courses, suggesting its effectiveness and relevance.
Creating a video course about learning science and note-taking as information processing strategies requires a foundational scientific understanding of this topic, followed by selecting, simplifying, and making it understandable for the public. The knowledge production process involves two steps. First, I establish a foundation of knowledge by gathering, organizing, and theorizing about how humans process information and take notes. In Step 2, I simplify and explain those fundamental concepts in a visually engaging way to make them more accessible to the public and students.
I draw on classic psychological study by Ebbinghaus (1885) and Bartlett (1932) to establish connections between human cognition, note-taking, and artificial intelligence, exploring the uniqueness of the human mind compared to computer memory.
This article aims to provide an overview of the intricate cognitive process involved in different note-taking strategies. I consider note-taking as an information tool for learning that is more reliable, low-cost, low-tech, widely accessible, easy to understand, and familiar to students. It introduces less risk in education. Note-taking sets a clear boundary compared to Large Language Models; it is a supportive tool in learning, rather than doing the learning itself. Fundamental understanding of note-taking techniques can help students choose the appropriate learning strategy for their individual needs.
From my research and understanding, various cognitive theories offer valuable insights into human information processing. One such theory is the Human Information Processing theory and related concepts such as encoding, retrieval, and storage. These concepts delve into aspects like working memory, cognitive load, and long-term memory, providing a comprehensive framework for understanding how humans process information.
Actually, the general public won't read lengthy and difficult-to-understand literature review explanations. They need content that is visually appealing and easy to understand. Below are work samples of explanatory articles with visually appealing illustrations, yet still science-based.