ES26 & ES294 @ Harvard
Unraveling AI and Human Cognition
Lecture: Tuesdays 09:45 AM - 12:30 PM @ 114 Western Ave Rm 2111
Lab: Mondays 18:00 PM - 19:30 PM @ 114 Western Ave Rm 2112
Office hour by Appointments
Lecture Materials
Lab Codes
Lab Schedule
Course Overview
ES 26 & ES294: AI and Human Cognition
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This class is offered for the first time in Harvard College and SEAS as a brand new interdisciplinary examination of the intricate relationship between artificial intelligence (AI) and human thought processes. Guided by Dr Fawwaz Habbal’s distinguished interactive lectures and practical recitations and Mr Edward Wang's practical lab sessions on training foundation models, the course spans both theoretical and practical dimensions of AI.
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Conceptual Foundation
Students investigate the nature of intelligence—both human and artificial—through discussions on metaphors of mind and machine, historical perspectives on AI, and the field’s evolving influence on human cognition. Critical reflection on the societal and ethical implications of AI ensures a comprehensive understanding of how AI mirrors and challenges our understanding of thought.
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Technical Perspectives
From early connectionist and symbolic models to cutting-edge hybrid architectures, the course chronicles AI’s historical roots and newest developments. Interactive discussions are a central feature: students enter each lecture having completed the readings, ready to debate, critique, and collaboratively generate new insights.
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Ethical & Philosophical Considerations
In parallel with technical topics, students engage with pressing ethical issues, including potential biases in AI, moral responsibilities of AI practitioners, and frameworks for human-centered design.
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Human-AI Collaboration
Through an exploration of AI-driven creativity, emotion recognition, and collaborative systems, students gain a deeper understanding of how human values, intuition, and empathy can inform the design and deployment of intelligent tools.
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Recitation Lab Component (Hands-On Experience)
These sessions bridge abstract theory and real-world application. Students develop foundational coding skills in Python, build simple chatbot models, utilize major industry APIs (OpenAI, Anthropic, Google, Meta), and learn to fine-tune large-scale AI systems. These practical experiences culminate in designing and implementing original AI projects.
Teaching Team
Spring 2025 Schedule
Lecture: Tuesday 09:45 AM - 12:30 PM
Lab: Monday 18:00 PM - 19:30 PM
Office hour by Apointments
**All course contents are subject to changes due to the fast moving nature**
Week 1: Jan-27
Lecture: Introduction
Course Overview, Human Systems
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Readings:
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Week 2: Feb-3
Lecture: The Nature of Intelligence
Definitions and theories of intelligence
Historical and cultural perceptions of AI
Difference between human and AI
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Readings:
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Week 3: Feb-10
Lecture: Computational Approaches
Connectionist models, Symbolic AI, Hybrid cognitive architectures
Computational approaches to model human cognition
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Readings:​​
Week 4: Feb-17
Lecture: Metaphors of Mind and Machines
Metaphors to conceptualize AI and human thought. Theories of mind and their implications for understanding AI
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Readings:
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Week 5: Feb-24
Week 6: Mar-3
Lecture: Human Element in AI
Human design and AI design principles and approaches. Importance of human values, emotions, empathy in AI development. AI-human alignment
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Readings:
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Lecture: AI and Human EmotionAffective computing and its implications on AI. Emotions in human decision-making and learning. Ethical implications of emotion recognition and manipulation by AI
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Readings:
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Week 7: Mar-10
Lecture: Philosophical Issues in AI​
Multi AI (Agents). AI rules, rights and restrictions. Societal impact of AI and human autonomy and agency
Readings:
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Week 8: Mar-17
Spring Recess
Week 9: Mar-24
Lecture: Human-Machine Collaboration
Opportunities and Challenges. AI impact on labor. Effective human-machine partnerships. Alternative perspective on AI's role in society
Readings:
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Week 10: Mar-31
Lecture: Cognitive Bias and Ai
Difficulty in creating alignment. Cognitive biases and implications for AI design and decision-making. AI systems reflect or mitigate biases.
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Readings:
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Week 11: Apr-7
Lecture: Project Presentation 1
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Readings:
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Week 12: Apr-14
Lecture: Project Presentation 2
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Readings:
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Week 13: Apr-21
Lecture: Reflction
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Readings:
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Week 14: Apr-28
Reading Period and Finals
Week 15: May-5
Reading Period and Finals
Assignments and Grading
Students learn in small groups.
There will be different types of submissions, these may vary per week.Homework is a collective effort. A small group of students work together together, they will: submit a weekly written reflection or creative exercise, lead the presentation of a weekly topic, and draft a final paper. Students may elect on their own too. More details will be discussed during our first meeting.
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Reflection:
After weekly readings, each small group will have a short discussion on the assigned readings and draft a reflection of about 1000 words. Every student in the small group must contribute to it. The reflection will include:
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Topic Definition: Identify key questions, theories and concepts.
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Critical Analysis: Question assumptions and explore strengths and weaknesses of arguments presented by the authors.
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Summarize your group discussion.
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Connection to Real-World Scenarios: relate to current events, real-world examples, or personal experiences. For example: implications and ethical considerations on various levels (social, moral, economic, environmental, etc.)
Creative Exercises:
Using a combination of Artificial Intelligence and Human Intelligence we will explore different modes of expression (Image, Video, Text, Sound). Here the group of the students will engage in the creation of new items and artifacts. These will address the implications and potential applications of AI and create a comparison of human-AI creations.
Presentation:
Every week, one of the small groups will be responsible for presenting a topic and leading a class discussion for about 60 minutes. The presentation should clearly explain the topic and define its implications. Some of these implications might be technical, while others may involve social considerations. Students can also introduce new ideas and provocations.
Final paper:
The group will choose an AI-cognition challenge and write a 1500-word paper discussing the concept and its implication.