I have been attending online sessions organized by Generative A.I. in the Teaching & Learning Group. This blog reflects my learning and important points discussed in the sessions.
Date: May 23, 2024
In Kimberly Pace Becker's talk titled "Constructing AI Literacy," she discusses the importance of AI literacy in academia and her journey from academia to co-founding Moxy. This company develops generative AI tools for research writing. She emphasizes that their AI tools act as coaches, not writers, aiding users in improving their writing skills.
Becker reflects on her background in applied corpus linguistics and her struggles with academic writing during her doctoral program, which inspired her to explore how generative AI can enhance learning and writing in academia. She highlights the polarized views on AI in education, stressing the need for timely engagement and ethical use of AI tools.
The talk includes a proposed AI literacy framework, discussing how generative AI can support learning in areas like workforce training, language learning, and aiding neurodivergent individuals. Becker emphasizes that AI should be used ethically and responsibly, comparing it to past technological concerns like Wikipedia.
She introduces functional literacy, rhetoric, and critical AI literacy, advocating for a balanced, non-polarized view of AI's role in writing and research. Becker underscores the importance of understanding the underlying principles of AI, like large language models and transformers, and the need for a critical approach to AI tools.
In conclusion, she encourages dialogue and collaboration in developing responsible AI use, suggesting practical steps for integrating AI literacy into teaching and learning practices.
The expert describes a method for efficiently creating a semester's worth of lesson plans in under an hour using AI tools. Leomi explains how to use Perplexity, an AI-powered search engine, and ChatGPT to gather and synthesize information. The process involves:
1. Understanding Perplexity: An AI search engine that summarizes top resources on a topic. Leomi highlights its efficiency and the advantages of a paid account.
2. Collecting Syllabi: Using Perplexity to find relevant syllabi on a chosen topic (e.g., differentiated learning for prospective teachers) by entering specific prompts to avoid unnecessary information.
3. Using ChatGPT: Uploading the collected syllabi to ChatGPT to create a new, synthesized syllabus. Leomi demonstrates how to provide context and use a framework called "Penguin prompting" to guide ChatGPT in structuring the course.
4. Creating Additional Materials: ChatGPT is also used to create lesson plans, PowerPoint outlines, and detailed scripts for each class. Leomi emphasizes the importance of iterating and conversing with ChatGPT to refine the output.
Key points include ensuring the accuracy of AI-generated content by cross-referencing resources, understanding the importance of detailed and well-structured prompts, and recognizing the role of generative AI as a tool to enhance productivity rather than replace human input entirely. Leomi also underscores the adaptability of this approach, allowing users to work on specific parts of the lesson planning process as needed.
Date: June 17, 2024
The talk titled "Co-Education: A Human-A.I. Collaboration Framework for Teaching and Learning" explores the intersection of artificial intelligence (AI) and education, presenting both optimistic and pessimistic views on their future integration. The speaker begins by acknowledging AI's disruptive arrival into education, highlighting its potential for transformative, personalized learning experiences in the optimistic scenario. This includes AI automating administrative tasks, enhancing teaching effectiveness, and improving student outcomes through adaptive learning platforms, innovative pedagogical methods, and increased engagement via gamification and virtual reality.
Conversely, the pessimistic view portrays AI as potentially leading to job losses, eroding human interaction, exacerbating inequalities, and compromising educational quality. Concerns include over-reliance on AI for teaching tasks, data privacy issues, biases in AI systems, and reduced creativity and critical thinking skills among students.
The speaker proposes a framework for human-AI collaboration in education, categorized into three levels:
1. AI as Assistant: AI handles routine tasks, providing support to teachers without overshadowing their roles in designing lessons and engaging directly with students.
2. Human-AI Co-Educators: AI collaborates with teachers in designing and delivering content, offering personalized learning paths and insights based on student data.
3. Autonomous AI Teaching and Learning Agents: AI autonomously drives significant parts of the learning process, with teachers overseeing and intervening as necessary to ensure educational quality and ethical standards.
The framework emphasizes the need for clear communication, continuous professional development, ethical considerations, and monitoring of AI's impact on education. It aims to maximize the benefits of AI while preserving and enhancing human potential and interaction within educational settings.
The speaker concludes by advocating for a balanced approach that combines optimism with caution, ensuring that AI integration in education enhances rather than diminishes human flourishing and addresses societal concerns effectively.
The Expert discusses the integration of generative AI in higher education, particularly focusing on automated assessment and feedback systems. The speaker begins by likening generative AI to the early skepticism faced by calculators, suggesting it will similarly become a ubiquitous tool in education. They outline a framework for AI integration, emphasizing its role in enhancing teaching and learning, reducing bias in grading, and offering personalized learning experiences. Case studies from Beacon House International College and Government College University Faisalabad highlight improvements in essay grading and programming assignments through AI. The talk concludes with the potential benefits and challenges of AI adoption in education.
Introduction to AI in education: Discusses the transformative potential of generative AI in automating assessment and feedback processes.
Traditional vs. automated assessment: Contrasts manual grading's limitations with AI's efficiency in grading and providing instant, consistent feedback.
Case studies: Presents two university case studies using AI for automated essay grading and programming assignments, showing significant performance improvements.
Challenges: Highlights technical integration challenges and the need for continuous improvement in AI tools.
Future implications: Discusses the potential of AI in fostering personalized learning experiences and enhancing educational outcomes.
I hope it was useful.
I hope it was useful.
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