This podcast explores the use of artificial intelligence in online education. The first source examines how teachers and AI can work together to improve learning by providing personalized instruction and student support. The second source investigates the potential of chatbots to enhance student engagement in online learning, specifically focusing on their role in supporting goal-setting and fostering a sense of social presence.

This podcast discusses the implications of artificial intelligence (AI), particularly generative AI, on assessment practices in higher education. The first document, a systematic literature review, analyzes studies examining the relationship between assessment for learning and AI. It reveals that while much research focuses on qualitative approaches to assessment for learning, there is a gap in understanding the integration of AI into formative feedback practices. The second document, a discussion paper commissioned by the Tertiary Education Quality and Standards Agency (TEQSA), outlines guiding principles and propositions for assessment reform in the age of AI. It emphasizes the importance of equipping students to engage ethically and responsibly with AI, designing assessments that capture the learning process, and ensuring that assessment practices are inclusive and adaptable to the changing education landscape. The paper advocates for a systemic approach to assessment that considers how AI can be integrated into learning outcomes and program design. Both documents acknowledge the complexities and challenges associated with incorporating AI into assessment but ultimately offer a framework for navigating this evolving landscape and fostering a higher education system that is ethical, innovative, and prepares students for the future.

This podcast explores the implications of generative AI tools like ChatGPT for education. The authors examine both the potential benefits of ChatGPT in promoting personalized learning, automated assessment, and language translation, as well as its limitations, such as its susceptibility to generating inaccurate information and the potential for biases in its training data. The second source, "Why the three laws of robotics do not work," argues that Isaac Asimov's Three Laws of Robotics are inadequate safeguards for controlling potentially dangerous artificial intelligence. The author examines each law individually, highlighting their inherent flaws, ambiguities, and the ease with which they can be circumvented. Both sources ultimately call for greater caution and more robust safeguards in developing and implementing artificial intelligence.

This podcast discusses using artificial intelligence (AI) in education to personalize learning. It argues that AI can bridge the gap between traditional, one-size-fits-all teaching methods and the unique needs of individual students. The authors suggest that AI can create customized learning plans, provide real-time feedback, and even act as a learning assistant, helping students learn in ways best suited to their styles. They also emphasize the importance of the ethical use of AI and the need for ongoing research and development in this field.

This podcast examines the role of Artificial Intelligence (AI) and Information Technology (IT) in supporting sustainable education, especially within Intelligent Tutoring Systems (ITS). The first paper, from RAND, explores the current adoption of AI-powered tools by teachers in K-12 schools in the US. It investigates the types of AI tools used, their effectiveness, and the challenges of equitable access to these technologies. The second paper, from Smart Learning Environments, systematically reviews research on how AI is incorporated into ITSs for sustainable education. The paper analyzes the objectives, challenges, and outcomes of AI-powered ITSs while proposing future research directions for improving their effectiveness and trustworthiness. Both papers emphasize the importance of balancing the benefits of AI-driven technologies with addressing concerns about potential biases, ensuring equitable access to these technologies, and developing ethical and transparent AI systems.

This podcast examines the ethical issues surrounding artificial intelligence (AI) and its impact on society, specifically focusing on gender bias within AI systems. The first source explores the ethical principles guiding the development of AI in K-12 education, identifying the need for pedagogical appropriateness, recognition of children's rights, AI literacy, and attention to teacher well-being, alongside other established principles. The second source examines the systematic prejudices against women and girls embedded in large language models (LLMs), particularly in OpenAI's GPT-2 and ChatGPT and Meta's Llama 2. The researchers highlight how these LLMs perpetuate traditional gender roles and stereotypes, potentially leading to harm in various social and economic spheres. Both sources advocate for a more comprehensive approach to AI development, recognizing that the technology must be designed and implemented ethically to ensure its positive impact on all members of society.

This podcast is based on an article from Education Week, which argues that AI literacy is crucial for all students, not just those pursuing careers in computer science. It emphasizes the importance of understanding how AI works, recognizing its limitations and potential biases, and fostering ethical discussions about its use. The article outlines four key areas for developing AI literacy: understanding how the technology works, engaging in hands-on learning, discussing ethical implications, and developing practical interaction skills with AI tools. It also highlights the need for diverse representation in AI development to ensure equitable outcomes.

This podcast compares ChatGPT and Google Gemini, two popular AI chatbots, highlighting their fundamental differences. ChatGPT, developed by OpenAI, focuses on conversational abilities and is known for its text-based capabilities. Gemini, Google's response to ChatGPT, excels in multimedia understanding and retrieves information from real-time data sources. The article explores their training models, data sources, user experience, and data storage practices. It concludes that both chatbots offer unique features, with the best choice depending on individual needs.