Dr. George Veletsianos
#ICYMI on April 28 with Dr. George Veletsianos. A recap summarized by Microsoft Copilot with slight edits is below:
Key Topics:
- Introduction and Setup: George Veletsianos and Peggy Semingson introduced the session and setting up the meeting. They confirmed the agenda and the structure of the day’s sessions. 1
- Generative AI in Education: George Veletsianos discussed the promises, tensions, and challenges surrounding generative AI in education. They emphasized a balanced approach without hype or panic, grounded in the history of educational technology and reflective practice. 2
- Historical Context of Educational Technology: George Veletsianos highlighted the recurring narrative of technology transforming education. They stressed the importance of questioning this idea critically, noting that technology often amplifies existing systems and inequalities rather than fundamentally changing education. 3
- Future of Higher Education: Participants were asked to imagine higher education in 2035, considering factors like online learning, AI integration, and environmental disruptions. This exercise aimed to explore potential futures and the implications for education. 4
- Tensions in AI Adoption: George Veletsianos outlined several tensions in AI adoption, including scholarly critique vs. pragmatic need, technological skepticism vs. innovation, and reflective pedagogy vs. efficiency gains. These tensions highlight the complexities of integrating AI into education. 5
- Assessment Strategies: The discussion included the need to rethink assessment strategies, focusing on the learning process rather than just the final product. This shift aims to understand better students’ learning journeys and the role of AI in supporting this process. 6
- Speculative Fiction Workshop: George Veletsianos led a workshop on creating speculative fiction to envision utopian AI educational futures. Participants engaged in brainstorming and writing exercises to imagine positive futures for education with thoughtful AI integration. 7
- Scenario Planning Activity: In the afternoon session, George Veletsianos introduced a scenario planning activity. Participants used challenge cards to discuss potential future scenarios for UTA and how the institution might respond to various challenges and opportunities. 8
- Group Discussions and Sharing: Participants in both in-person and online groups discussed the challenge cards and shared their insights. They considered risks, opportunities, and strategies for UTA to navigate potential future scenarios. 9
- Workshop Conclusion: George Veletsianos concluded the workshop by encouraging participants to reflect on how the discussions expanded their thinking about AI in education. They emphasized the importance of imagining positive futures and taking small steps towards those visions. 10
- Resources: Discussion Questions: UTA-Workshop – Google Drive
Henrik Skaug Sætra
Tuesday, April 29, 2025: Henrik Skaug Sætra presented Hybrid and Collective Intelligence and Connections to Climate: A recap summarized by Microsoft Copilot with slight edits is below:
Generated by AI. Be sure to check for accuracy.
Meeting notes:
- Meeting Setup: Henrik and Pete discussed the technical setup for the meeting, including audio and slide sharing, and confirmed the participation of Peggy, who would join virtually later.
- Technical Setup: Henrik and Pete discussed the technical setup for the meeting, including ensuring the audio was working correctly and that Henrik could share his slides. They also addressed some initial technical issues, such as pixelation and frame drops, and Henrik adjusted his settings to improve the connection.
- Peggy’s Participation: Peggy was confirmed to join the meeting virtually at around 10:40. Pete mentioned that she might assist in the second part of the meeting, depending on the group size and the need to break into smaller groups.
- Workshop Structure: Henrik and Pete outlined the structure of the workshop, including a keynote presentation followed by a workshop session, with a focus on artificial intelligence and its implications for academia.
- Keynote Presentation: Henrik’s keynote presentation focused on the impact of artificial intelligence on academia, particularly in research and education. He discussed the potential changes and challenges posed by AI technologies.
- Workshop Session: The workshop session was designed to be interactive, allowing participants to engage in exercises and discussions about the implications of AI in academia. Henrik planned to use collaborative online tools to gather feedback and insights from the participants.
- AI in Academia: Henrik presented on the impact of intelligence technology on academia, focusing on research and education, and discussed the potential changes and challenges posed by AI.
- Impact on Research: Henrik discussed how AI technologies could significantly impact research processes, including data gathering, analysis, and dissemination. He highlighted the potential for AI to automate various stages of research, which could lead to increased efficiency but also raised concerns about the loss of human involvement in critical thinking and analysis.
- Impact on Education: Henrik explored the implications of AI in education, particularly in higher education. He discussed how AI could be used to enhance learning experiences, provide personalized education, and support educators in administrative tasks. However, he also noted the challenges of integrating AI into educational systems and the potential risks of over-reliance on technology.
- Challenges and Concerns: Henrik raised several concerns about the widespread adoption of AI in academia, including issues related to data privacy, ethical considerations, and the potential for AI to perpetuate biases. He emphasized the need for careful regulation and oversight to ensure that AI technologies are used responsibly and ethically.
- Historical Context of AI: Henrik provided a historical overview of AI development, highlighting key milestones and the evolution of AI technology over the years.
- Early Developments: Henrik traced the history of AI back to the 1940s and 1950s, mentioning key figures such as McCullough and Pitts, who proposed the concept of artificial neurons, and the Dartmouth Conference in 1956, which is often considered the birthplace of AI as a field of study.
- Key Milestones: Henrik highlighted significant milestones in AI development, including the creation of the first AI programs, the development of expert systems in the 1980s, and the victory of IBM’s Deep Blue over chess champion Garry Kasparov in 1997. He also mentioned more recent advancements, such as the success of AlphaGo in defeating human champions in the game of Go.
- Generative AI: Henrik discussed the emergence of generative AI technologies, such as ChatGPT, which have brought AI into mainstream awareness. He noted that these technologies have demonstrated impressive capabilities in generating human-like text and performing various tasks, leading to increased interest and investment in AI research and applications.
- Perspectives on AI: Henrik discussed various perspectives on AI, including its role as a tool, its impact on collective intelligence, and the concept of hybrid intelligence, where humans and machines collaborate.
- AI as a Tool: Henrik explained the perspective of AI as a tool that can be used to enhance human capabilities. He emphasized that while AI can be a powerful tool, it is essential to consider how it is used and the potential consequences of its application.
- Collective Intelligence: Henrik discussed the concept of collective intelligence, where AI systems can contribute to the collective knowledge and problem-solving abilities of groups. He highlighted examples of how AI can be integrated into collaborative efforts to achieve better outcomes than individual efforts alone.
- Hybrid Intelligence: Henrik introduced the idea of hybrid intelligence, where humans and AI systems work together in a complementary manner. He described how AI can augment human intelligence by providing computational power, pattern recognition, and data analysis capabilities, while humans contribute creativity, empathy, and contextual understanding.
- Future Scenarios: Henrik presented three potential future scenarios for AI in academia: collaborative intelligence, business as usual, and technological acceleration, and discussed their implications.
- Collaborative Intelligence: Henrik described a future scenario where AI and humans collaborate closely, with AI systems augmenting human capabilities and providing support in research and education. This scenario emphasizes the importance of explainable AI and the need for regulation to ensure ethical and responsible use of AI technologies.
- Business as Usual: In the business as usual scenario, Henrik outlined a future where AI adoption continues at its current pace, with incremental improvements and integration into existing systems. This scenario assumes that AI will be used to optimize and automate various processes, but without significant changes to the overall structure of academia.
- Technological Acceleration: Henrik discussed a more radical future scenario where AI development accelerates rapidly, leading to the emergence of superhuman AI researchers and significant disruptions in academia. In this scenario, AI systems take on more autonomous roles, potentially surpassing human capabilities in research and innovation, and raising concerns about the implications for human researchers and educators.
- Research Automaton: Henrik introduced the concept of the research automaton, exploring the potential for AI to automate various stages of the research process, and discussed the benefits and challenges of such automation.
- Automation Potential: Henrik explored the potential for AI to automate different stages of the research process, including ideation, literature review, data gathering, analysis, writing, and dissemination. He highlighted the benefits of increased efficiency and the ability to handle large volumes of data.
- Challenges of Automation: Henrik discussed the challenges associated with automating the research process, such as the risk of losing critical thinking and creativity, the potential for biases in AI-generated outputs, and the ethical considerations of relying heavily on AI for research.
- Human Involvement: Henrik emphasized the importance of maintaining human involvement in the research process to ensure the quality and integrity of research. He argued that while AI can be a valuable tool, it should not replace the human elements of curiosity, intuition, and ethical judgment in research.
- Interactive Exercise: Henrik led an interactive exercise where participants evaluated the impact of AI on different stages of the research process, using a collaborative online tool to gather feedback.
- Exercise Overview: Henrik introduced an interactive exercise where participants used a collaborative online tool to evaluate the impact of AI on various stages of the research process. Participants were asked to place stamps on a matrix to indicate their views on the benefits and challenges of using AI in each stage.
- Participant Feedback: Participants provided feedback on the use of AI in different research stages, highlighting areas where AI could be beneficial, such as transcription and data analysis, and areas where it might be problematic, such as peer review and theoretical development. The exercise facilitated a discussion on the potential and limitations of AI in research.
- Concerns and Resistance: Henrik expressed concerns about the potential negative impacts of AI on research and academia, advocating for a cautious approach and resistance to full automation.
Resources shared in the chat in Henrik’s session:
https://www.anthropic.com/research/exploring-model-welfare
https://link.springer.com/article/10.1007/s12124-020-09523-6
https://journals.sagepub.com/doi/10.3233/FRL-200023
https://link.springer.com/article/10.1007/s43681-021-00092-x
