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.