Ethan Mollick, Lilach Mollick, Natalie Bach, LJ Ciccarelli, Ben Przystanski, Daniel Ravipinto, AI Agents and Education: Simulated Practice at Scale.

Abstract: This paper explores the potential of generative AI in creating adaptive educational simulations. By leveraging a system of multiple AI agents, simulations can provide personalized learning experiences, offering students the opportunity to practice skills in scenarios with AI-generated mentors, role-players, and instructor-facing evaluators. We describe a prototype, PitchQuest, a venture capital pitching simulator that showcases the capabilities of AI in delivering instruction, facilitating practice, and providing tailored feedback. The paper discusses the pedagogy behind the simulation, the technology powering it, and the ethical considerations in using AI for education. While acknowledging the limitations and need for rigorous testing, we propose that generative AI can significantly lower the barriers to creating effective, engaging simulations, opening up new possibilities for experiential learning at scale.

Ethan Mollick and Lilach Mollick, Instructors as Innovators: a Future-focused Approach to New AI Learning Opportunities, With Prompts.

Abstract: 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.

Ethan Mollick, Co-Intelligence: Living and Working with AI (: Penguin Portfolio, 2024)

Abstract: Something new entered our world in November 2022 — the first general purpose AI that could pass for a human and do the kinds of creative, innovative work that only humans could do previously. Wharton professor Ethan Mollick immediately understood what ChatGPT meant: after millions of years on our own, humans had developed a kind of co-intelligence that could augment, or even replace, human thinking. Through his writing, speaking, and teaching, Mollick has become one of the most prominent and provocative explainers of AI, focusing on the practical aspects of how these new tools for thought can transform our world.In Co-Intelligence, Mollick urges us to engage with AI as co-worker, co-teacher, and coach. He assesses its profound impact on business and education, using dozens of real-time examples of AI in action. Co-Intelligence shows what it means to think and work together with smart machines, and why it’s imperative that we master that skill. Mollick challenges us to utilize AI’s enormous power without losing our identity, to learn from it without being misled, and to harness its gifts to create a better human future. Wide ranging, hugely thought-provoking, optimistic, and lucid, Co-Intelligence reveals the promise and power of this new era.

Lennart Meincke, Ethan Mollick, Christian Terwiesch, Prompting Diverse Ideas: Increasing AI Idea Variance.

Abstract: Unlike routine tasks where consistency is prized, in creativity and innovation the goal is to create a diverse set of ideas. This paper delves into the burgeoning interest in employing Artificial Intelligence (AI) to enhance the productivity and quality of the idea generation process. While previous studies have found that the average quality of AI ideas is quite high, prior research also has pointed to the inability of AI-based brainstorming to create sufficient dispersion of ideas, which limits novelty and the quality of the overall best idea. Our research investigates methods to increase the dispersion in AI-generated ideas. Using GPT-4, we explore the effect of different prompting methods on Cosine Similarity, the number of unique ideas, and the speed with which the idea space gets exhausted. We do this in the domain of developing a new product development for college students, priced under $50. In this context, we find that (1) pools of ideas generated by GPT-4 with various plausible prompts are less diverse than ideas generated by groups of human subjects (2) the diversity of AI generated ideas can be substantially improved using prompt engineering (3) Chain-of-Thought (CoT) prompting leads to the highest diversity of ideas of all prompts we evaluated and was able to come close to what is achieved by groups of human subjects. It also was capable of generating the highest number of unique ideas of any prompt we studied.

Ethan Mollick and Lilach Mollick, Assigning AI: Seven Approaches for Students, with Prompts.

Abstract: This paper examines the transformative role of Large Language Models (LLMs) in education and their potential as learning tools, despite their inherent risks and limitations. The authors propose seven approaches for utilizing AI in classrooms: AI-tutor, AI-coach, AI-mentor, AI-teammate, AI-tool, AI-simulator, and AI-student, each with distinct pedagogical benefits and risks. Prompts are included for each of these approaches. The aim is to help students learn with and about AI, with practical strategies designed to mitigate risks such as complacency about the AI’s output, errors, and biases. These strategies promote active oversight, critical assessment of AI outputs, and complementarity of AI's capabilities with the students' unique insights. By challenging students to remain the "human in the loop," the authors aim to enhance learning outcomes while ensuring that AI serves as a supportive tool rather than a replacement. The proposed framework offers a guide for educators navigating the integration of AI-assisted learning in classrooms.  

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Fabrizio Dell'Acqua, Edward McFowland, Ethan Mollick... et al. (Working), Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality.

Abstract: The public release of Large Language Models (LLMs) has sparked tremendous interest in how humans will use Artificial Intelligence (AI) to accomplish a variety of tasks. 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. Further, our analysis shows the emergence of two distinctive patterns of successful AI use by humans along a spectrum of human-AI integration. One set of consultants acted as “Centaurs,” like the mythical halfhorse/half-human creature, dividing and delegating their solution-creation activities to the AI or to themselves. Another set of consultants acted more like “Cyborgs,” completely integrating their task flow with the AI and continually interacting with the technology.    

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Ethan Mollick and Lilach Mollick (Working), Using AI to Implement Effective Teaching Strategies in Classrooms: Five Strategies, Including Prompts.

Abstract: This paper provides guidance for using AI to quickly and easily implement evidence-based teaching strategies that instructors can integrate into their teaching. We discuss five teaching strategies that have proven value but are hard to implement in practice due to time and effort constraints. We show how AI can help instructors create material that supports these strategies and improve student learning. The strategies include providing multiple examples and explanations; uncovering and addressing student misconceptions; frequent low-stakes testing; assessing student learning; and distributed practice. The paper provides guidelines for how AI can support each strategy, and discusses both the promises and perils of this approach, arguing that AI may act as a “force multiplier” for instructors if implemented cautiously and thoughtfully in service of evidence-based teaching practices.

Valentina Assenova and Ethan Mollick (Working), Tipping Points in Gender Representation: Evidence from the Startup Game.

Abstract: Startups founded by women consistently receive less funding and lower valuations compared to those founded by men, particularly from male investors. A common remediation technique to reduce this gender inequality has been to increase women’s representation in powerful resource-controlling roles, though in some cases, this appears to exacerbate backlash and stereotyping effects. We examine whether and how shifts in the gender composition of venture capitalists and entrepreneurs might affect the emergence of a critical mass to remediate gender inequality. We propose that a central driver of this inequality is homophily preferences among men and suggest that these preferences become weaker as women’s representation increases. This mechanism results in a nonlinear “Goldilocks” effect: gender inequality dips for median levels of women’s representation but is amplified below and above these thresholds. We examine the evidence for the Goldilocks effect using data from 27,082 participants in a live-play simulation of fundraising in Silicon Valley, in which we randomized players to the roles of founders of early-stage companies and partners of venture capital firms. We found that gender inequality was lowest at median thresholds of critical mass in women’s representation but increased below and above these thresholds. Our findings suggest that increasing gender diversity in venture capital firms holds the key to mitigating gender inequality in startup funding and valuation.

Ethan Mollick, The Unicorn\’s Shadow: Combating the Dangerous Myths that Hold Back Startups, Founders, and Investors (: Wharton University Press, 2020)

Jason Greenberg and Ethan Mollick (Working), Sole Survivors: Solo Ventures Versus Founding Teams.

Abstract: A widespread scholarly and popular consensus suggests that new ventures perform better when launched by teams, rather than individuals. This view has become so pervasive that many of the foremost investors rarely, if ever, fund startups founded by a solo entrepreneur. Despite this belief in the superiority of teams in the startup process, little empirical evidence has been used to examine this key question. In this paper, we examine the implications of founding alone versus as a group by using a unique dataset of crowdfunded companies that together generated approximately $358 million in total revenue. We show that companies started by solo founders survive longer than those started by teams. Further, organizations started by solo founders generate more revenue than organizations started by founder pairs, and do not perform significantly different than larger teams. This suggests that the taken-for-granted assumption among scholars that entrepreneurship is best performed by teams should be reevaluated, with implications for theories of team performance and entrepreneurial strategy.