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AI for Faculty - Teaching and Learning in the Age of AI
Welcome to this learning journey.To start, click on one of the modules to access its content.Whenever you see a button, click on it to reveal additional information and resources.
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AI for Faculty - Teaching and Learning in the Age of AI
1. Redefining the Professor’s Role in the Age of AI
3. Being a Student in the Age of AI: Critical Thinking at Stake
2.New Teaching Practices
Redefining the Professor’s Role in the Age of AI
6. Investigate your teaching identity
1. A Shift in the Professor’s Role
Survey
Discussion with AI
5. The Professor as a Learning Designer: Working with AI
2. A Changing Role: The HEC View
Street Interview
Podcast
3. A Renewed Role for Professors
4. Education Beyond the Clock:The Future of the Teacher in the Age of AI
Video
Article
MAIN MENU
New Teaching Practices
5. Integrating AI into Assignments: Insights from Harvard
1. Lessons from International Experiments
Survey
Reading
2. Use Case with Laurence Lehmann-Ortega
4. Use Case with Georg Wernicke
Testimonial
Testimonial
3. Use Case with Jeremy Ghez
MAIN MENU
Testimonial
Being a Student in the Age of AI: Critical Thinking at Stake
7. Designing a Level-Based AI Policy in Higher Education
1. What is Critical Thinking?
Reading
Video
2. What it means to be a student in the age of Generative AI
6. Positioning Ourselves Toward the Nature of AI Intelligence
Video
Video
3. Two learning configurations
5. Learner–machine symbiosis: a contradictory injunction
Video
Video
4. The Cognitive Cost of AI Intimacy
MAIN MENU
Video
A Great Title
We better grasp visual content. Visual content is associated with cognitive and psychological mechanisms. Things enter through the eyes, the first image is what matters. We associate visual content with emotions.
Link
A great title
We better understand visual content. Visual content is associated with cognitive and psychological mechanisms. Things enter through the eyes, the first image is what matters. We associate visual content with emotions.
Link
If the nature of AI intelligence remains philosophically debated, educational policy does not need to wait for a definitive answer. This video proposes a graduated approach: the way we frame and authorize AI use should depend on students’ level of cognitive maturity and disciplinary expertise. Rather than promoting full symbiosis or radical reform, it argues for a pedagogy of “right distance” — one that evolves from cautious, limited use in early years to more advanced, reflective collaboration at doctoral level.
If generative AI creates an intimacy dividend for students — offering a space free of judgment and friction — it also comes at a cost. This video explores how close, seamless interaction with AI can blur the boundaries between students’ own reasoning and machine-generated suggestions. When students lose track of their intellectual process, they struggle to account for their work — raising important questions about dependency, vigilance, and what it means to genuinely “do” the work of being a student.
The first survey shows how faculty are already using AI — primarily to support preparation and content creation. The second survey takes the story one step further: beyond current practices, where are faculty willing to go next — and where do concerns remain? Together, these findings reveal an important distinction: faculty feel comfortable using AI to enhance their own productivity, but are more cautious when AI directly affects student evaluation or decision-making. Understanding this difference is essential when thinking about responsible AI integration in teaching.
A cool title
We understand visual content better. Visual content is associated with cognitive and psychological mechanisms. Things enter through the eyes, the first image is what counts. We associate visual content with emotions.
+ info
In this video, we shift our focus from teaching practices to the conditions under which students now carry out their academic work in the age of generative AI. Drawing on a sociological perspective, this resource explores how AI tools are not simply efficiency boosters, but intimate companions that reshape how students “perform” their role and make their intellectual work visible and legitimate. Rather than asking whether AI replaces critical thinking, we examine how students use these tools to sustain their identity as learners — and how this creates new tensions and hybrid forms of academic work.
A great title
We better understand visual content. Visual content is associated with cognitive and psychological mechanisms. Things enter through the eyes, the first image is what matters. We associate visual content with emotions.
Link
A great title
We better grasp visual content. Visual content is associated with cognitive and psychological mechanisms. Things enter through the eyes, the first image is what counts. We associate visual content with emotions.
+ info
If the professor’s role is evolving — from transmitter to mediator — what does this shift look like in the concrete work of course design? This podcast explores how professors design learning experiences in partnership with artificial intelligence. Drawing on the work of Diana Laurillard and Rose Luckin, it examines how AI can function as a cognitive assistant in course design — generating options, supporting iteration, and extending reflective capacity. At the same time, the episode underscores a central theme running through this module: professional judgment remains indispensable. Ethical vigilance, pedagogical intention, and disciplinary coherence cannot be delegated. Rather than automating teaching, working with AI can deepen creativity, strengthen reflection, and reinforce the distinctly human dimensions of learning.
This article invites you to step into a near future where artificial intelligence has quietly entered the classroom — not as a replacement for the teacher, but as a powerful new presence. Through concrete examples, global data, and thoughtful reflection, it explores a central question: if AI can explain, personalize, and even assess, what becomes of the educator’s role? Rather than offering technical guidance, this text provides perspective. It challenges us to reconsider how we define teaching, value impact, and measure contribution in an age where knowledge is instant and adaptive. Read it not as a prediction, but as an invitation — to reflect on what must evolve in our institutions, and what must remain deeply human in our profession.
AI is not just another digital tool. According to Professor Laurence Leman Ortega, it represents a transformation in how students learn — and therefore in how we teach. In this conversation, she reflects on how she has rethought her pedagogy and integrated AI into her courses in a structured and meaningful way.
If intimacy with AI creates cognitive tension, asking students to consciously manage this hybridization creates an additional burden. This video challenges the idea that students should become skilled “managers” of their own fusion with AI. Instead, it argues that the responsibility lies not with individual vigilance, but with the design of learning environments that structurally protect students’ intellectual autonomy.
We now turn to a more structured reflection: if the professor’s role is indeed evolving, how can we conceptualize that shift? Beyond the emergence of new tools, a deeper question arises: what becomes of the professor when knowledge is instantly accessible and generative systems can explain, summarize, and even simulate expertise? This video invites us to rethink teaching not as transmission, but as mediation. In a context where information is abundant, the professor’s role evolves toward structuring meaning, exercising professional judgment, and creating the conditions for deep learning. AI does not diminish academic expertise — it clarifies it. It challenges us to articulate what remains irreducibly human in teaching: discernment, ethical responsibility, and the capacity to transform knowledge into understanding.
Throughout this module, we have examined the evidence of change, listened to diverse perspectives, and explored how the professor’s role is evolving — from transmitter to mediator, from content expert to learning designer. The question now becomes personal. How is your professional identity shifting? What will you choose to preserve, to adapt, or to rethink in your own teaching practice? This final activity invites you to move from analysis to reflection. Rather than offering another framework, it offers a dialogue — a structured conversation with AI designed to help you clarify your stance, surface your priorities, and translate reflection into action. The paradigm shift is collective. The response, however, is individual.
A cool title
We understand visual content better. Visual content is associated with cognitive and psychological mechanisms. Things enter through the eyes, the first image is what counts. We associate visual content with emotions.
+ info
According to the Digital Education Council Global AI Faculty Survey 2025, 64% of faculty believe AI will bring significant to transformative change to the role of professors. Only a small minority expect minimal impact. While most anticipate change, many acknowledge that its precise shape remains unclear. These findings suggest more than incremental adjustment. They point toward a potential paradigm shift in the role of the professor — from transmitter of knowledge to mediator, designer, and critical guide in an AI-augmented environment. The question is therefore not whether the role will evolve, but how faculty will define and lead that evolution.
We’ve already seen examples from HEC Paris on how AI can enhance pedagogy—supporting content creation, student engagement, and new teaching formats. Now let’s look at how another leading institution is rethinking assessments and assignments in response to generative AI. In this Harvard Magazine article, Harvard instructors explain how they are redesigning homework and evaluation so that students not only use AI tools, but also critically engage with and justify their use of AI outputs — a shift toward deeper learning outcomes in the era of AI.
Following the global findings of the Digital Education Council survey, which point to a significant shift in how faculty perceive the impact of AI on their role, this street-interview video brings the conversation closer to home. In this video, professors and students share their own perspectives on how artificial intelligence is reshaping the professor’s role in practice. Through their voices, we move from aggregated data to lived experience — exploring evolving expectations, new responsibilities, and the changing dynamics of teaching and learning in the age of AI.
In this interview, Professor Jeremy Ghez explains how he uses AI as a partner in his teaching practice. From class preparation to pedagogical experimentation, he shares how AI supports productivity, clarity, and continuous improvement.
Providing students with a dedicated AI teaching assistant seems like a natural step.Yet in practice, usage was limited.In this interview, Georg discusses what he tried, what he observed, and what this reveals about how students actually engage with AI tools.
In this module, we focus on students and the development of critical thinking in the age of generative AI. As these tools become part of everyday learning, an important question emerges: how can we continue to support and evaluate critical thinking in this new environment? Critical thinking involves questioning, analysing, interpreting and evaluating information in order to make informed judgments (Monash University; Facione, 1990). In other words, it goes beyond knowing facts — it requires evaluating evidence, questioning assumptions, and justifying conclusions. However, recent data show that many students struggle to critically analyse AI-generated content, and 78% of faculty members are concerned about students’ ability to evaluate AI outputs. These findings highlight a key challenge: as AI becomes embedded in learning practices, educators must rethink how critical thinking is developed and demonstrated.
A great title
We better grasp visual content. Visual content is associated with cognitive and psychological mechanisms. Things come in through our eyes, the first image is the one that matters. We associate visual content with emotions.
+ info
Building on the analysis of how generative AI reshapes the student role, this video introduces two distinct configurations of student–AI interaction. In the first configuration, AI extends familiar academic practices — helping students revise, organize knowledge, and prepare for exams without fundamentally altering their role. In the second, AI becomes a more intimate space of interaction, where students disclose doubts and difficulties without fear of judgment — reshaping the emotional and social conditions of learning.
If learner–machine symbiosis is problematic, part of the difficulty lies in the very nature of AI intelligence. Generative AI does not think as humans do — its cognitive architecture is fundamentally different. In this video, we explore why teachers must position themselves philosophically and pedagogically toward this form of intelligence, and how different conceptions of AI lead to different educational uses.
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Transcript
START
AI for Faculty - Teaching and Learning in the Age of AI
Welcome to this learning journey.To start, click on one of the modules to access its content.Whenever you see a button, click on it to reveal additional information and resources.
NEXT
AI for Faculty - Teaching and Learning in the Age of AI
1. Redefining the Professor’s Role in the Age of AI
3. Being a Student in the Age of AI: Critical Thinking at Stake
2.New Teaching Practices
Redefining the Professor’s Role in the Age of AI
6. Investigate your teaching identity
1. A Shift in the Professor’s Role
Survey
Discussion with AI
5. The Professor as a Learning Designer: Working with AI
2. A Changing Role: The HEC View
Street Interview
Podcast
3. A Renewed Role for Professors
4. Education Beyond the Clock:The Future of the Teacher in the Age of AI
Video
Article
MAIN MENU
New Teaching Practices
5. Integrating AI into Assignments: Insights from Harvard
1. Lessons from International Experiments
Survey
Reading
2. Use Case with Laurence Lehmann-Ortega
4. Use Case with Georg Wernicke
Testimonial
Testimonial
3. Use Case with Jeremy Ghez
MAIN MENU
Testimonial
Being a Student in the Age of AI: Critical Thinking at Stake
7. Designing a Level-Based AI Policy in Higher Education
1. What is Critical Thinking?
Reading
Video
2. What it means to be a student in the age of Generative AI
6. Positioning Ourselves Toward the Nature of AI Intelligence
Video
Video
3. Two learning configurations
5. Learner–machine symbiosis: a contradictory injunction
Video
Video
4. The Cognitive Cost of AI Intimacy
MAIN MENU
Video
A Great Title
We better grasp visual content. Visual content is associated with cognitive and psychological mechanisms. Things enter through the eyes, the first image is what matters. We associate visual content with emotions.
Link
A great title
We better understand visual content. Visual content is associated with cognitive and psychological mechanisms. Things enter through the eyes, the first image is what matters. We associate visual content with emotions.
Link
If the nature of AI intelligence remains philosophically debated, educational policy does not need to wait for a definitive answer. This video proposes a graduated approach: the way we frame and authorize AI use should depend on students’ level of cognitive maturity and disciplinary expertise. Rather than promoting full symbiosis or radical reform, it argues for a pedagogy of “right distance” — one that evolves from cautious, limited use in early years to more advanced, reflective collaboration at doctoral level.
If generative AI creates an intimacy dividend for students — offering a space free of judgment and friction — it also comes at a cost. This video explores how close, seamless interaction with AI can blur the boundaries between students’ own reasoning and machine-generated suggestions. When students lose track of their intellectual process, they struggle to account for their work — raising important questions about dependency, vigilance, and what it means to genuinely “do” the work of being a student.
The first survey shows how faculty are already using AI — primarily to support preparation and content creation. The second survey takes the story one step further: beyond current practices, where are faculty willing to go next — and where do concerns remain? Together, these findings reveal an important distinction: faculty feel comfortable using AI to enhance their own productivity, but are more cautious when AI directly affects student evaluation or decision-making. Understanding this difference is essential when thinking about responsible AI integration in teaching.
A cool title
We understand visual content better. Visual content is associated with cognitive and psychological mechanisms. Things enter through the eyes, the first image is what counts. We associate visual content with emotions.
+ info
In this video, we shift our focus from teaching practices to the conditions under which students now carry out their academic work in the age of generative AI. Drawing on a sociological perspective, this resource explores how AI tools are not simply efficiency boosters, but intimate companions that reshape how students “perform” their role and make their intellectual work visible and legitimate. Rather than asking whether AI replaces critical thinking, we examine how students use these tools to sustain their identity as learners — and how this creates new tensions and hybrid forms of academic work.
A great title
We better understand visual content. Visual content is associated with cognitive and psychological mechanisms. Things enter through the eyes, the first image is what matters. We associate visual content with emotions.
Link
A great title
We better grasp visual content. Visual content is associated with cognitive and psychological mechanisms. Things enter through the eyes, the first image is what counts. We associate visual content with emotions.
+ info
If the professor’s role is evolving — from transmitter to mediator — what does this shift look like in the concrete work of course design? This podcast explores how professors design learning experiences in partnership with artificial intelligence. Drawing on the work of Diana Laurillard and Rose Luckin, it examines how AI can function as a cognitive assistant in course design — generating options, supporting iteration, and extending reflective capacity. At the same time, the episode underscores a central theme running through this module: professional judgment remains indispensable. Ethical vigilance, pedagogical intention, and disciplinary coherence cannot be delegated. Rather than automating teaching, working with AI can deepen creativity, strengthen reflection, and reinforce the distinctly human dimensions of learning.
This article invites you to step into a near future where artificial intelligence has quietly entered the classroom — not as a replacement for the teacher, but as a powerful new presence. Through concrete examples, global data, and thoughtful reflection, it explores a central question: if AI can explain, personalize, and even assess, what becomes of the educator’s role? Rather than offering technical guidance, this text provides perspective. It challenges us to reconsider how we define teaching, value impact, and measure contribution in an age where knowledge is instant and adaptive. Read it not as a prediction, but as an invitation — to reflect on what must evolve in our institutions, and what must remain deeply human in our profession.
AI is not just another digital tool. According to Professor Laurence Leman Ortega, it represents a transformation in how students learn — and therefore in how we teach. In this conversation, she reflects on how she has rethought her pedagogy and integrated AI into her courses in a structured and meaningful way.
If intimacy with AI creates cognitive tension, asking students to consciously manage this hybridization creates an additional burden. This video challenges the idea that students should become skilled “managers” of their own fusion with AI. Instead, it argues that the responsibility lies not with individual vigilance, but with the design of learning environments that structurally protect students’ intellectual autonomy.
We now turn to a more structured reflection: if the professor’s role is indeed evolving, how can we conceptualize that shift? Beyond the emergence of new tools, a deeper question arises: what becomes of the professor when knowledge is instantly accessible and generative systems can explain, summarize, and even simulate expertise? This video invites us to rethink teaching not as transmission, but as mediation. In a context where information is abundant, the professor’s role evolves toward structuring meaning, exercising professional judgment, and creating the conditions for deep learning. AI does not diminish academic expertise — it clarifies it. It challenges us to articulate what remains irreducibly human in teaching: discernment, ethical responsibility, and the capacity to transform knowledge into understanding.
Throughout this module, we have examined the evidence of change, listened to diverse perspectives, and explored how the professor’s role is evolving — from transmitter to mediator, from content expert to learning designer. The question now becomes personal. How is your professional identity shifting? What will you choose to preserve, to adapt, or to rethink in your own teaching practice? This final activity invites you to move from analysis to reflection. Rather than offering another framework, it offers a dialogue — a structured conversation with AI designed to help you clarify your stance, surface your priorities, and translate reflection into action. The paradigm shift is collective. The response, however, is individual.
A cool title
We understand visual content better. Visual content is associated with cognitive and psychological mechanisms. Things enter through the eyes, the first image is what counts. We associate visual content with emotions.
+ info
According to the Digital Education Council Global AI Faculty Survey 2025, 64% of faculty believe AI will bring significant to transformative change to the role of professors. Only a small minority expect minimal impact. While most anticipate change, many acknowledge that its precise shape remains unclear. These findings suggest more than incremental adjustment. They point toward a potential paradigm shift in the role of the professor — from transmitter of knowledge to mediator, designer, and critical guide in an AI-augmented environment. The question is therefore not whether the role will evolve, but how faculty will define and lead that evolution.
We’ve already seen examples from HEC Paris on how AI can enhance pedagogy—supporting content creation, student engagement, and new teaching formats. Now let’s look at how another leading institution is rethinking assessments and assignments in response to generative AI. In this Harvard Magazine article, Harvard instructors explain how they are redesigning homework and evaluation so that students not only use AI tools, but also critically engage with and justify their use of AI outputs — a shift toward deeper learning outcomes in the era of AI.
Following the global findings of the Digital Education Council survey, which point to a significant shift in how faculty perceive the impact of AI on their role, this street-interview video brings the conversation closer to home. In this video, professors and students share their own perspectives on how artificial intelligence is reshaping the professor’s role in practice. Through their voices, we move from aggregated data to lived experience — exploring evolving expectations, new responsibilities, and the changing dynamics of teaching and learning in the age of AI.
In this interview, Professor Jeremy Ghez explains how he uses AI as a partner in his teaching practice. From class preparation to pedagogical experimentation, he shares how AI supports productivity, clarity, and continuous improvement.
Providing students with a dedicated AI teaching assistant seems like a natural step.Yet in practice, usage was limited.In this interview, Georg discusses what he tried, what he observed, and what this reveals about how students actually engage with AI tools.
In this module, we focus on students and the development of critical thinking in the age of generative AI. As these tools become part of everyday learning, an important question emerges: how can we continue to support and evaluate critical thinking in this new environment? Critical thinking involves questioning, analysing, interpreting and evaluating information in order to make informed judgments (Monash University; Facione, 1990). In other words, it goes beyond knowing facts — it requires evaluating evidence, questioning assumptions, and justifying conclusions. However, recent data show that many students struggle to critically analyse AI-generated content, and 78% of faculty members are concerned about students’ ability to evaluate AI outputs. These findings highlight a key challenge: as AI becomes embedded in learning practices, educators must rethink how critical thinking is developed and demonstrated.
A great title
We better grasp visual content. Visual content is associated with cognitive and psychological mechanisms. Things come in through our eyes, the first image is the one that matters. We associate visual content with emotions.
+ info
Building on the analysis of how generative AI reshapes the student role, this video introduces two distinct configurations of student–AI interaction. In the first configuration, AI extends familiar academic practices — helping students revise, organize knowledge, and prepare for exams without fundamentally altering their role. In the second, AI becomes a more intimate space of interaction, where students disclose doubts and difficulties without fear of judgment — reshaping the emotional and social conditions of learning.
If learner–machine symbiosis is problematic, part of the difficulty lies in the very nature of AI intelligence. Generative AI does not think as humans do — its cognitive architecture is fundamentally different. In this video, we explore why teachers must position themselves philosophically and pedagogically toward this form of intelligence, and how different conceptions of AI lead to different educational uses.