This week’s prompt is to define AI for use in education based on the readings. I like defining AI simply as software, though it is a new generation of software with enhanced capabilities to solve problems in new ways, especially by learning. The question is whether something can be described as AI without machine learning done by technology independent of a human. Machine learning is one type of a “constellation of technologies” (Clark,2020) that make up the AI landscape.
Reinforcement learning is an example of AI that relies on a human as part of the iterative process of improving the tool’s processes and outputs. Humans provide feedback on what the AI has done in relation to the prompt or parameters of the output. This feedback improves the next iteration of what actions the technology takes. What qualifies as AI? Though I like the definition of AI as software, not all software has artificial intelligence. Tools that perform the tasks they were explicitly programmed to do, even as an automated function, do not qualify as AI. What does qualify are the tools that mimic or simulate human intelligence. This includes bounded problem solutions, such as translation and text-to-speech or speech-to-text. One of my classes used the Packback tool, which scores student discussion posts according to a “Curiosity Score” (Packback, n.d.). This tool can grade the quality of discussion posts against criteria the instructor creates, and provide immediate feedback to students on whether they met that criteria. I appreciated the immediate feedback it provided, as I believe that is such an important component of learning. References Clark, D. (2020). Artificial Intelligence for Learning: How to use AI to support employee development. Kogan Page Limited. Packback. (n.d.). Packback for Educators. Retrieved 01/28/2024 from https://www.packback.co/educators/.
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This week's readings for my technology-based learning environment class helped me consider three aspects of online learning that are worth considering for my project to design a semester-long course: problem-based learning, ChatGPT as a facilitator for self-directed learning (SDL), and online discussions.
The first reading on problem-based learning (PBL) (Savery & Duffy, 1995) underscores the value of the learner's active engagement in tasks that mirror real-world scenarios. A learner-centered approach can encourage learners to construct their own knowledge within a context similar to where they would apply it. It's a shift from traditional learning where content is delivered and quizzed, to a more dynamic process where learners identify learning issues, evaluate resources, and learn from these resources. The process of PBL doesn't guarantee that all content objectives will be realized in a given problem, but that's the beauty of it - learning isn't linear. Instead, PBL offers multiple opportunities to reinforce concepts that may not have been fully grasped earlier. But this means we have to be willing to offer a variety of learning experiences that may lead to the same goals and objectives. Over a semester-long course, the way I’m currently thinking about this is in revisiting certain concepts as they layer and reinforce each other. I chose to read “Exploring the Role of ChatGPT as a Facilitator for Motivating Self-Directed Learning Among Adult Learners,” (Lin, 2023) for the assignment because of my interest exploring where self-directed learning techniques are appropriate, along with a conviction that sometimes they aren’t the right approach. The article highlighted many ways that ChatGPT can help facilitate a self-directed learning experience. If I have my own learning goal, ChatGPT is a great resource for many of the activities the article described. However, with all the student effort it described, I wondered why there was a need for a formal learning experience in the first place. The article did highlight some limitations and challenges for such an approach, such as student’s ability to critically evaluate the quality of resources. My opinion is that most students need a level of direction and resource curation within specific bounds. But students in a purely or mostly online environment do need a higher level of self-directed learning skills than students who are coming to a classroom. My peer-selected reading (Breivik, 2020) intrigued me as an exploration of the practice on including online discussions as assignments. My experience in this (100% online) graduate program appears to be backed by the study in this article. The view is that online discussions can be a good place for reflection, but they tend to fall short of achieving some of the learning objectives they may be attempting to support. Of course, given my thoughts on PBL on reinforcing learning with different activities, that may be perfect fine. This article was focused on specific argumentation skills, and showed that the discussion as an assignment failed. But it’s interesting to note the author believes that the discussion was still successful as a way to discuss knowledge from their readings and exchange ideas (Breivik, 2020). These readings have me thinking about where scenarios or case studies and online discussion forums can be effective activities for achieving the learning objectives in my course design. I’m especially thinking about where I want to use self-directed activities and where I want to support learners with synchronous activities. Note: I drafted this post with the help of NotionAI, using my highlights and notes from these readings. To learn more about the first part of this workflow, check out this previous post. References Breivik, J. (2020). Argumentative patterns in students’online discussions in an introductory philosophy course. Nordic Journal of Digital Literacy, 15(1), 8–23. https://doi.org/10.18261/issn.1891-943x-2020-01-02 Lin, X. (2023). Exploring the Role of ChatGPT as a Facilitator for Motivating Self-Directed Learning Among Adult Learners. Adult Learning, 1. https://doi-org.libproxy.library.unt.edu/10.1177/10451595231184928 Savery, J. R., & Duffy, T. M. (1995). Problem based learning: An instructional model and its constructivist framework. Educational Technology, 35, 31–38 Over the past few years, I've been somewhat aware of the increasing integration of AI into many aspects of my life. What first caught my attention was the map app on my first iPhone (around 2012?), providing directions and real-time feedback on traffic conditions. It was transformational for me in getting around in Dallas. Recently, I've been using a pair of apps in my own learning, especially relevant in my graduate studies. Reader and Readwise are connected apps that serve as a "read-it-later" app and a service to help with remembering what you read.
I use Reader to handle digital content. Whether it's a webpage or a PDF document, I add it to Reader for a more focused reading experience. The app allows me to highlight key points that resonate with me and add notes for additional context. I also use it to highlight headings and tag them accordingly, creating a structured outline of the content. The AI capabilities of Reader have been particularly useful, offering features such as summarizing the document and asking questions of it. I like Readwise for its daily review emails. If my original reading wasn't digital, I can manually enter my notes from the physical book into Readwise. The app has algorithms to resurface my highlights and notes, and even questions I choose, to remind me of (and help me recall) what I've read. The control settings allow me to manage how often certain types of content make the email, including articles, books, and even books I've marked as read, but not highlighted. My interest in AI for learning and teaching primarily stems from my curiosity about how technology can leverage good learning science. I've observed how quickly businesses can adapt new technology, and I'm intrigued to see how this can be implemented in an educational context. As my experience with Reader and Readwise illustrates, I believe AI can offer specific solutions that enhance the learning experience. I do have some concerns about the potential harm from AI. The way it is being used in China in elementary schools (The Wall Street Journal, 2019), brings to mind dystopian images of George Orwell's Big Brother from his novel, 1984 (Orwell, 2021). However, privacy and misuse are my primary concerns at this point. I like calling AI software, rather than any kind of "intelligence." The comment Clark (2020) cites from Roger Schank, that "AI is merely software and that we should in fact just call it software" really resonated with me. Having worked in software for many years now, I understand that software can help solve problems, and that iteration and constant improvement is part of its DNA. It's imperative that we bring human intelligence to the oversight of what problems we use computers to solve and how they solve them. References Clark, D. (2020). Artificial Intelligence for Learning: How to use AI to support employee development. Kogan Page Limited. Orwell, G. (2021). Nineteen Eighty-Four. Penguin Classics. The Wall Street Journal. (Oct 1, 2019). How China is Using Artificial Intelligence in Classrooms [Video]. YouTube. https://youtu.be/JMLsHI8aV0g?si=Jr52iLn3DbShz4F7 |
AuthorMichele Wiedemer has worked in software as an "accidental instructional designer" for many years. She is currently completing the MS in Learning Technologies at The University of North Texas. This blog represents reflections on specific assignments in the coursework. Archives
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