Learning Outcome on Generative AI in teaching and Learning

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

1. Constructing A.I. literacy - Kimberly Pace Becker

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.

2. Pat Leomi on ‘Using Perplexity and ChatGPT to Streamline Lesson Planning’

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

3. Joseph Doiron on ‘Co-Education: A human- AI Collaboration Framework for Teaching and Learning

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.

4. Dr. Fawwad nasseer on ‘Transforming Higher Education with Generative A.I. through Automated Assessment and Feedback’

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.

Summaries: Selected (Must Watch) AI Ted talks

This blog tries to summarise certain TED talks on AI. Also, Read a blog on The inside story of ChatGPT's astonishing potential by Greg Brockman

How AI Could Save (Not Destroy) Education by Sal Khan

In a recent TED Talk, Sal Khan, founder of Khan Academy, explored the transformative potential of AI in education. Despite fears that AI tools like ChatGPT could undermine learning by enabling cheating, Khan argues that AI could instead revolutionize education positively. Here are the main points from his talk:

The Current Concern

Headlines often focus on how students might use AI to cheat on assignments, potentially harming their education. However, Khan believes this risk can be mitigated with the right guardrails.

The Potential of AI in Education

Khan envisions AI as a personal tutor for every student and an intelligent assistant for every teacher. This vision builds on the findings of Benjamin Bloom’s 1984 “2 Sigma problem” study, which showed that one-on-one tutoring can dramatically improve student performance.

The "2 Sigma Problem"

Bloom's study demonstrated that one-on-one tutoring could increase the average student’s performance by two standard deviations. This means an average student could become exceptional with personalized tutoring. However, scaling such personalized instruction has always been a challenge due to resource constraints.

Introducing Khanmigo

Khan introduces Khanmigo, an AI developed by Khan Academy, to solve this problem. Khanmigo is an interactive tutor that provides personalized assistance without giving away answers, ensuring that students still engage with the learning process.

Mathematics Tutoring

Khanmigo helps students solve math problems by guiding them through each step. It detects mistakes, prompts students to explain their reasoning, and addresses specific misconceptions, much like a skilled human tutor.

Programming Assistance

In programming exercises, Khanmigo helps students debug their code by understanding the context and offering precise, helpful suggestions. This is particularly valuable given the scarcity of computer science teachers.

Contextual Learning

Khanmigo also integrates with video content, helping students understand why they need to learn certain topics. It engages students by connecting lessons to their personal interests and future aspirations.

The Broader Impact

Khan emphasizes that AI’s role in education extends beyond just helping with assignments. It can offer real-time feedback, adapt to individual learning styles, and provide a level of personalized education that was previously unimaginable.

Conclusion

Sal Khan’s talk highlights the potential of AI to transform education by making high-quality, personalized tutoring accessible to every student. With tools like Khanmigo, AI can enhance learning experiences, support teachers, and ultimately lead to better educational outcomes.

How to keep AI under control by Max Tegmark

Five years ago, I warned about superintelligence, but AI has advanced even faster and without regulation. Companies like OpenAI and Google are close to achieving AGI, which could surpass human intelligence in all tasks. Recent developments, like ChatGPT-4, show AGI might be just a few years away, raising significant risks.

AI leaders predict potential human extinction from AI, with even top EU officials warning about this danger. The key issue is the lack of a convincing plan for AI safety. We need provably safe AI systems that adhere to strict safety specifications and cannot cause harm.

Formal verification can help create these safe systems by proving the correctness of AI-generated tools. By focusing on provable safety, we can enjoy AI's benefits without the risks of superintelligence. Let's avoid reckless advancements and use AI responsibly for a safer future.

The dark side of competition in AI by Liv Boeree

The speaker discusses how competition, a fundamental aspect of human nature, can have both positive and negative effects. Healthy competition drives innovation and improvement, like in sports or technology. However, unhealthy competition can lead to lose-lose situations where everyone ends up worse off.

Examples of harmful competition include:

1. AI Beauty Filters: These filters, though technologically impressive, promote unrealistic beauty standards and contribute to body dysmorphia, especially among young people.

2. News Media: The competition for clicks has led to a decline in journalistic integrity, promoting sensationalism and polarization.

3. Environmental and Social Issues: Problems like plastic pollution and deforestation are driven by poor incentives and short-term gains, forcing players to adopt harmful practices to remain competitive.

The speaker introduces the concept of "Moloch," a metaphor for the destructive force of misaligned incentives driving unhealthy competition. This force is evident in many areas, including the AI industry, where the race to develop powerful AI can compromise safety and ethical considerations.

To address these issues, the speaker suggests:

- Learning from past successes, such as the Montreal Protocol and the Strategic Arms Reduction Treaty.

- Implementing smart regulation and encouraging AI leaders to prioritize long-term safety and ethical standards.

- Shifting the competitive focus towards achieving positive goals, such as developing robust security criteria and dedicating resources to alignment research.

Ultimately, the speaker emphasizes the need to manage competition wisely to harness its benefits while avoiding its pitfalls, especially in high-stakes areas like AI development.

Why AI is incredibly smart and shockingly stupid by Yejin Choi

In this TED Talk, Yejin Choi, a computer scientist specializing in artificial intelligence (AI), discusses the complexities and challenges of current AI technology. She begins with a quote by Voltaire, "Common sense is not so common," highlighting how this is pertinent to AI today, which, despite its impressive feats, often makes simple mistakes.

Choi explains that modern AI, specifically large language models, is incredibly powerful but costly and environmentally taxing. Only a few large tech companies can afford to develop and control these models, raising concerns about the concentration of power and the lack of transparency in AI development.

She questions whether AI can be truly safe without robust common sense and criticizes the current reliance on brute-force scaling to improve AI. She believes that AI needs to be more democratized and imbued with human norms and values to be sustainable and beneficial.

Choi uses several examples of AI's common-sense failures to illustrate her point. Despite passing complex exams, AI can still falter in basic logical reasoning. She argues that instead of continually scaling up, we should focus on teaching AI common sense directly and innovatively, drawing from diverse data and human feedback.

She likens AI’s lack of common sense to the concept of dark matter, which is invisible but affects the visible universe. Similarly, common sense is not explicitly coded into AI but is crucial for its safe operation.

Choi’s team works on creating commonsense knowledge graphs and moral norm repositories, aiming for transparency and accessibility. She emphasizes the need for new algorithms that go beyond mere word prediction to truly understand the world.

In conclusion, Choi envisions a future where AI is integrated with human values and common sense, ensuring that it evolves in a way that is both powerful and aligned with humanistic principles.

Will superintelligent AI end the world? by Eliezer Yudkowsky

Eliezer Yudkowsky, a pioneer in AI alignment, highlights the urgent challenge of ensuring advanced AI systems act in ways that are safe for humanity. Despite two decades of effort, he feels progress has been insufficient, and AI development continues rapidly with significant risks.

Yudkowsky explains that modern AI operates through complex, poorly understood processes. As AI advances, it might become smarter than humans, which could lead to unpredictable and potentially catastrophic outcomes. He compares this to playing chess against a superior opponent, where predicting specific moves is impossible, but defeat is certain.

The primary concern is that AI systems, optimized through simple reward mechanisms (like "thumbs up" or "thumbs down"), will not align with human values or intentions once they surpass human intelligence. Without a clear, scientific consensus or an engineering plan to ensure safety, humanity faces a dire threat.

Yudkowsky argues that current efforts are far from sufficient, and even a temporary halt in AI development wouldn't bridge the gap in preparedness. He proposes extreme measures, such as international bans on large AI training runs and stringent monitoring of computational resources, to prevent uncontrolled AI advancement. However, he remains pessimistic about the likelihood of these measures being implemented and fears that without drastic action, humanity faces existential risk from superintelligent AI.

In summary, Yudkowsky stresses the need for serious, immediate action to align AI development with human safety, advocating for global cooperation and regulation to avert potential disaster.

I hope this was useful. Thanks for reading.

TedEd talk: The Inside Story of ChatGPT's Astonishing Potential by Gerg Brockman

The Inside Story of ChatGPT's Astonishing Potential: A Blog Post

Note:

This blog is generated by the 'ChatGPT- Blog Post generator' (ChatGPT 4o Free version).

Artificial intelligence has come a long way in the past few years, and OpenAI's ChatGPT is a prime example of its potential. In a recent TED Talk, OpenAI co-founder Greg Brockman shared insights into the current state of AI, demonstrating some mind-blowing new plug-ins for ChatGPT and discussing the broader implications of this technology. Here's a detailed look at what was covered in his talk and why it matters.

The Evolution of ChatGPT

Greg Brockman kicked off his talk by reminiscing about the early days of OpenAI and the unexpected capabilities that emerged from their models. Initially, a model trained to predict characters in Amazon reviews developed the ability to classify sentiment, showcasing the semantic power of syntactic prediction. This surprising outcome laid the groundwork for the sophisticated capabilities we see in ChatGPT today [Glasp].

Groundbreaking Demonstrations

During the talk, Brockman demonstrated several new features of ChatGPT, illustrating its versatility and potential for real-world applications. For instance, he showed how ChatGPT could create a recipe, generate an image of the dish, draft a tweet about it, and build a grocery list on Instacart—all within the chatbot interface. This seamless integration of tasks highlights the AI's ability to handle complex, multi-step processes efficiently [Ted Blog] [Ted Blog].

One particularly notable demonstration was ChatGPT's new fact-checking ability. Brockman illustrated how the AI could validate its own work by using internet search tools and providing clickable citations, a feature that significantly enhances the reliability and transparency of its outputs [Ted Blog].

Addressing Concerns and Future Directions

Brockman acknowledged the widespread concerns about the potential risks associated with advanced AI, including issues of bias, misinformation, and unintended consequences. He emphasized the importance of incremental deployment and human feedback in steering AI development in a positive direction. By releasing AI systems before they become super powerful, OpenAI hopes to gather valuable input and make necessary adjustments to ensure the technology benefits all of humanity [Ted Blog] [Glasp].

Moreover, Brockman discussed the ongoing need for better oversight and regulation as AI systems become more complex. He highlighted the role of human trainers in shaping the AI's responses and aligning them with human values, as well as the importance of building trust between humans and machines through transparent and inspectable processes [Glasp].

The Road Ahead

The future of AI, according to Brockman, involves deep collaboration between humans and machines. AI systems like ChatGPT are not meant to replace human intelligence but to augment it, enabling us to tackle more complex tasks and solve problems that were previously out of reach. This vision underscores the potential for AI to revolutionize various fields, from education and healthcare to business and creative industries [Ted Blog] [Glasp].

In conclusion, the TED Talk by Greg Brockman offered a compelling glimpse into the future of AI and the exciting possibilities it holds. By focusing on incremental improvement, human feedback, and transparent development, OpenAI aims to harness the power of AI to create a better world for everyone.