Un blog comme support de cours

Rethinking pedagogy in times of AI: a collective endeavor at ESSEC Business School

By Estefania Santacreu Vasut, Associate Dean for Pedagogy, and the K-lab team.

Three years ago, online and hybrid teaching became the new norm in higher education, increasing the need for educators to manage their students’ attention. Active learning, group work and shorter sessions accelerated as ways to adapt pedagogy to the attention economy. While in person teaching has resumed, managing attention remains important. 

Today, the advent of ChatGPT has showcased how beyond managing attention, AI pushes us to consider also how to nurture intention. To rethink pedagogy in times of AI, ESSEC Business School has adopted a collaborative approach, gathering the input of its teaching community, program directors, departments, the Metalab1, Chief Data Officer, the K-lab2 and its team of instructional designers and librarians. 

As part of this effort, the pedagogical workshop of April 19th, 2023 brought together more than a hundred participants on the topic “Opportunities and challenges of ChatGPT from a pedagogical perspective.”3

It was structured around how AI may impact program needs and opportunities, change evaluation methods, course creation and teaching opportunities and how to take into account technical and ethical issues. Three groups worked on each of these dimensions. Groups worked prior to the workshop and presented their conclusions/recommendations. 

The first group, led by the Academic Director of the Master in Management program and Professor of Marketing Emmanuelle LeNagard, presented an analysis on how AI may influence program needs and opportunities. The group focused on the short term impacts, not forbidding the use of such tools but developing a set of good practices including, for instance, mentioning when AI was used to write an essay or perform an assignment. 

The second group, led by Associate Professor of Economics Anastasios Dosis, worked on  thinking about possible faculty guidelines such as establishing an AI policy for each course. Departments are currently working on adapting such guidelines to different academic fields. 

The third group, led by Metalab’s Executive Director Benoit Bergeret discussed the expected evolution of ChatGPT and generative AIs, including technical and ethical issues as well as issues related to data protection.

Overall, three main principles emerged from this collective effort: Adapt, Train and Evolve. Adapting to AI means neither forbiding nor embracing it blindly. To achieve this, training our community to AI including students but also teachers and administrators is essential. Indeed, leveling the playing field early on may reduce the risks that AI exacerbates inequalities down the road (in the labor market). Last but not least, making our pedagogy evolve means remaining open and aware of on-going developments, partnering with companies to better understand how AI will impact the labor market and society at large4. ESSEC Business school has a history of collectively leading the way in promoting innovative pedagogical approaches. While AI may pose new challenges and opportunities, our collective approach remains, more than ever, relevant and needed.

1ESSEC Metalab for Data, Technology & Society constitutes one of the three major axes of the ESSEC RISE strategy. It fosters interdisciplinary scientific research and pedagogical innovation, leveraged by the on-going transformations in data and AI technologies, in the fields of economics and management, humanities, as well as statistical and engineering sciences.

2The K-lab team provides resources and services for pedagogy and research at ESSEC.

3Pedagogical workshops are co-organized by the Associate Dean for Pedagogy and the K-lab on a regular basis. ESSEC Faculty and external lecturers are invited to participate. The purpose of those workshops is to share experiments, good practices or emerging issues within the teaching community at ESSEC.

4The American Chamber of Commerce in France and ESSEC Metalab have conceived and written a white paper after eight months’ work by AI practitioners, AmCham members and academic representatives: Access the White Paper