Practical AI Videos
Our Crash Course video series on AI is a practical guide for educators and students designed to provide an introduction to the world of Large Language Models. The series begins with an introduction to Large Language Models, with a focus on OpenAI’s ChatGPT and Microsoft’s Bing Chat, which includes an overview of how the models generate output and their transformative impact on our work and teaching. The second video delves deeper into the specifics of each model, providing practical examples and guidelines for effective use. The third video focuses on how to prompt the AI, teaching users how to guide the AI and integrate their own expertise into the interaction. The fourth and fifth videos are tailored for educators and students who can use AI to enhance their teaching and learning.
Our research
Current papers include:
Instructors as Innovators: a Future-focused Approach to New AI Learning Opportunities, With Prompts
This paper explores how instructors can leverage generative AI to create personalized learning experiences for students that transform teaching and learning. We present a range of AI-based exercises that enable novel forms of practice and application including simulations, mentoring, coaching, and co-creation. For each type of exercise, we provide prompts that instructors can customize, along with guidance on classroom implementation, assessment, and risks to consider. We also provide blueprints, prompts that help instructors create their own original prompts. Instructors can leverage their content and pedagogical expertise to design these experiences, putting them in the role of builders and innovators. We argue that this instructor-driven approach has the potential to democratize the development of educational technology by enabling individual instructors to create AI exercises and tools tailored to their students' needs. While the exercises in this paper are a starting point, not a definitive solutions, they demonstrate AI's potential to expand what is possible in teaching and learning.
New Modes of Learning Enabled by AI Chatbots: Three Methods and Assignments.
In this paper, we discuss the opportunity provided by AI because it can help us teach in new ways. The AI's flaws —its tendency to make up facts, its lack of nuance, and its ability to make excellent student essays — can be used to improve education. This isn’t for some future theoretical version of AI. Instructors can create assignments right now, using ChatGPT, that will help stretch students in new ways.
In the rush to deliver AI benefits directly to students, the role of teachers is often overlooked. AI tutors, as exciting as they are, do not replace the complex role of a teacher in front of a class. But not enough effort seems to be going toward applying AI to help instructors. We have a new paper that tries to remedy that gap by providing some research-backed approaches to pedagogy and the AI prompts (for GPT-4, GPT-3.5, and other AIs) to implement them
Assigning AI: Seven Approaches for Students, with Prompts
The incredible promise of AI as a way for students all over the world, of all ability levels, to learn is undeniable. Education is our most powerful system for increasing social mobility, unlocking potential, and improving lives. A tool that can help with this has tremendous implications. Plus, students are already using AI for direct help. Teaching them how to do it responsibly may alleviate some of the negative implications of our AI moment. In this paper, we tackle ways that students can be assigned to use AI directly. We don’t shy away from the dangers but provide detailed instructions on how students and instructors can think about each of the tools we suggest.
Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality (Dell'Acqua, Fabrizio and McFowland, Edward and Mollick, Ethan R. and Lifshitz-Assaf, Hila and Kellogg, Katherine and Rajendran, Saran and Krayer, Lisa and Candelon, François and Lakhani, Karim R)
In our study conducted with Boston Consulting Group, a global management consulting firm, we examine the performance implications of AI on realistic, complex, and knowledge-intensive tasks. The pre-registered experiment involved 758 consultants comprising about 7% of the individual contributor-level consultants at the company. After establishing a performance baseline on a similar task, subjects were randomly assigned to one of three conditions: no AI access, GPT-4 AI access, or GPT-4 AI access with a prompt engineering overview. We suggest that the capabilities of AI create a “jagged technological frontier” where some tasks are easily done by AI, while others, though seemingly similar in difficulty level, are outside the current capability of AI. For each one of a set of 18 realistic consulting tasks within the frontier of AI capabilities, consultants using AI were significantly more productive (they completed 12.2% more tasks on average, and completed task 25.1% more quickly), and produced significantly higher quality results (more than 40% higher quality compared to a control group). Consultants across the skills distribution benefited significantly from having AI augmentation, with those below the average performance threshold increasing by 43% and those above increasing by 17% compared to their own scores. For a task selected to be outside the frontier, however, consultants using AI were 19 percentage points less likely to produce correct solutions compared to those without AI