MIT Study Finds Chatgpt May Harm Critical Thinking Skills

In early June 2025, researchers at MIT’s Media Lab quietly dropped a bombshell preprint titled “Your Brain on ChatGPT: Accumulation of Cognitive Debt…” (albertoromgar.medium.com, media.mit.edu). Their controlled experiment with 54 college-aged participants — measured via EEG during essay writing — has ignited new debates on generative AI’s impact on cognition and learning. 


What the Study Did?

  • Three groups, three writing sessions:

    1. Brain-only – wrote without any assistance

    2. Google users – used web search

    3. LLM users – wrote with ChatGPT’s help
      (media.mit.edu, economictimes.indiatimes.com)

  • Session 4 involved tool-switching: LLM→Brain-only and Brain-only→LLM paths. (media.mit.edu)

  • Methods: EEG measured real-time brain connectivity, essays assessed via NLP, teachers, and an AI judge. (media.mit.edu)


Key Findings

  1. Diminished neural engagement ✨

    • Brain-only authors displayed strongest EEG connectivity across frontal-parietal networks, tied to creativity and memory.

    • Google users ranked in the middle.

    • ChatGPT users showed the least brain activity, up to a 55% reduction in dDTF connectivity. (media.mit.edu, theregister.com, m.economictimes.com)

  2. Cognitive offloading & passivity

    • ChatGPT users increasingly relied on copy-and-paste. By session 3, many simply input the prompt, reviewed output, and were done.

    • They struggled to recall or quote their own writing, reporting minimal sense of ownership. (thetimes.co.uk)

  3. Language quality & critique decline

    • Essays from the LLM group were more formulaic, homogeneous, and perceived by evaluators as “soulless.” (thetimes.co.uk)

    • Google users performed better but still lagged behind Brain-only peers in critical engagement.

  4. Switching tools matters

    • Participants who began unaided then used ChatGPT exhibited higher connectivity and memory recall — a hybrid “scaffolded” path that seemed beneficial. (edtechinnovationhub.com)

    • But going from LLM‑to‑Brain didn’t immediately restore their engagement — underlining the lingering cognitive debt.


Implications: What Does It All Mean?

Benefit Risk
Efficiency & convenience ⚠️ Cognitive atrophy – diminished critical thinking, memory, deeper learning
Supports scaffolding – when used after thoughtful effort ⚠️ Dependency risk – habit-building reliance when started early
Tool-assisted learning ⚠️ Copy-paste tendency – less personal reflection, ownership, nuance

Lead researcher Nataliya Kosmyna warns that while generative AI offers shortcuts, it carries “cognitive cost” — reducing engagement in memory, planning, and evaluation (media.mit.edu, edtechinnovationhub.com, arxiv.org, time.com). Though not peer-reviewed yet, it raises pressing concerns for classrooms already integrating AI.


A Balanced Approach

Experts emphasize moderation and strategy:

  • Use ChatGPT after you’ve tried writing independently — then refine or brainstorm with AI.

  • Resist letting it generate content wholesale; engage actively with AI outputs.

  • Educators should teach prompt strategies, critical analysis of AI answers, and help students maintain ownership of their work.

A Cambridge researcher cautioned:

“AI could foster a kind of ‘laziness’ … but context matters — tutor use boosts, tool-as-crutch diminishes.” (thetimes.co.uk)


Final Thoughts

MIT’s study sends a powerful signal: our brains are sensitive to how we engage with AI. The effortless—but passive—support of ChatGPT can dull creativity, memory, and intellectual ownership. But when used thoughtfully, it can enhance learning.

As conversation turns to AI’s role in academia, this research reminds us: balance matters. Tech should amplify thinking, not substitute for it.

For Further Reading

Communication Systems form the backbone of modern digital infrastructure—from mobile networks and satellite links to Wi-Fi and optical fibers. Whether you're an electrical engineering student, a computer science enthusiast, or a self-learner pursuing deeper knowledge, this guide curates the most highly recommended resources to help you self-study Communication Systems effectively and rigorously.


📘 1. Textbooks You Shouldn't Skip

These books are widely regarded as classics in the field—ideal for deep conceptual understanding and mathematical rigor.

  • ✔️ Principles of Communication Systems by Herbert Taub & Donald Schilling
    - Foundational with clear explanations and detailed derivations.
    - Great for first-time learners and thorough review.
  • ✔️ Communication Systems by Simon Haykin
    - A gold standard for undergrad and grad-level courses.
    - Emphasizes theory with practical examples, including modern topics.
  • ✔️ Digital and Analog Communication Systems by Leon W. Couch
    - Beginner-friendly, visually supported explanations.
    - Excellent supplement if you find Haykin too dense.

🎓 2. Free University Courses (Video Lectures)

  • ✔️ MIT OpenCourseWare – 6.011 Signals, Systems and Inference
    - Taught by Prof. Alan Oppenheim, a signal processing legend.
    - Covers key foundations and modern systems with assignments and exams.
  • ✔️ NPTEL – Communication Systems by Prof. Aditya K. Jagannatham (IIT Kanpur)
    - Well-structured and detailed, great for GATE prep.
    - Covers AM/FM, sampling, quantization, and more.
  • ✔️ Stanford – EE261: The Fourier Transform and Its Applications by Prof. Brad Osgood
    - Essential for understanding modulation and filtering.
    - Conceptually rich and accessible for all learners.

🔧 3. Simulation & Practice Tools

  • ✔️ MATLAB & Simulink
    - Industry-standard simulation tool.
    - Try the official MATLAB Onramp and build modulation/BER projects.
  • ✔️ GNU Radio
    - Free open-source SDR simulation software.
    - Great for wireless signal processing experimentation.
  • ✔️ Python + Scipy/NumPy
    - Lightweight and flexible for building your own systems.
    - Check out: pysdr.org for tutorials and projects.

🧠 4. Problem Solving & Concept Reinforcement

  • ✔️ Schaum’s Outline of Analog and Digital Communications by Hwei P. Hsu
    - 700+ solved problems, ideal for practice and revision.
  • ✔️ GATE ECE Previous Year Papers
    - Excellent conceptual MCQs and short answer practice.
    - Useful even outside India for testing core understanding.

🎧 5. Supplementary Media & Blogs

  • ✔️ YouTube Channels: ECE Academy, Neso Academy, All About Electronics
    - Visual and bite-sized explanations for quick reviews.
  • ✔️ All About Circuits – Communications Section
    - Free and beginner-friendly online text.
    - Covers practical systems and core concepts well.

🧩 6. Suggested Study Plan (Beginner to Intermediate)

  1. Start with NPTEL or MIT OCW lectures.
  2. Read Haykin or Taub for theoretical grounding.
  3. Solve Schaum’s problems weekly to reinforce learning.
  4. Build mini MATLAB or Python projects (e.g., FM Transmitter).
  5. Experiment with GNU Radio if hardware is available.
  6. Test your understanding using GATE or practice exams.

🚀 Final Tips

  • Start with a good Signals & Systems foundation.
  • Focus on modulation, demodulation, bandwidth, and SNR.
  • Brush up on Fourier and probability theory.
  • Apply your knowledge through simulations and mini-projects.

📚 Conclusion

Mastering Communication Systems through self-study is absolutely achievable. With the right blend of theory, practice, and simulation, you can confidently build the knowledge needed for exams, research, or industry applications. Whether you're prepping for GATE, exploring SDRs, or just intellectually curious, these resources will serve as your roadmap.

Have a favorite resource or tool? Drop it in the comments section!

This is a self-directed learning journey modeled on the structure, values, and methodology of the Open Source Society University (OSSU) curriculum. While this curriculum is not officially affiliated with OSSU, it embraces their open-access philosophy and is designed to remain free, community-driven, and continuously evolving.

🎯 Purpose 

The aim of this curriculum is to guide independent learners through a comprehensive, interdisciplinary study of Environmental Science—entirely free of cost and at your own pace. In 2025, with climate literacy more crucial than ever, this pathway empowers anyone to gain deep, technical, and applied knowledge in environmental systems and sustainability.

Due to the vast and multidisciplinary nature of environmental science, this curriculum may not be exhaustive. You’re encouraged to contribute—whether it's adding updated resources, suggesting new modules, or refining the learning paths.

🌱 Core Curriculum

  • Problem Solving for Scientists Learn logical and structured approaches to scientific challenges using modern computational tools.

  • Introduction to Environmental Science Explore global ecosystems, biodiversity, pollution, and sustainability challenges facing the 21st century.

  • Ecology Understand population dynamics, trophic interactions, and ecosystem-level processes.

  • Differential Equations I A mathematical foundation for modeling natural systems and processes.

  • Environmental Physics and Chemistry Delve into the physical and chemical principles underlying atmospheric, terrestrial, and aquatic systems.

  • Fluid Mechanics Study fluid flow in natural environments, with applications in oceanography, meteorology, and hydrology.

  • Data Analysis Apply statistical and data science tools to analyze environmental datasets using Python or R (2025’s top open tools).

  • Differential Equations II Advanced techniques for solving complex system models relevant to environmental engineering.

  • Transport Processes Learn how energy, matter, and contaminants move through the environment.

  • Hydrology Analyze the water cycle, watershed modeling, and water resource engineering.

  • Probability and Statistics Build a strong statistical foundation for uncertainty analysis in environmental modeling.

  • Modeling and Simulation Use tools like MATLAB, Python, or Julia to simulate climate systems, ecosystems, or pollution transport. 

Problem Solving

Courses

Duration

Effort

Creative Problem Solving and Decision Making

5 weeks

6 hours/week


Introduction to Environmental Science

Courses

Duration

Effort

Introduction to Environmental Science

4 weeks

5 hours/week

Climate Change: The Science

7 weeks

3 hours/week


Ecology

Courses

Duration

Effort

Ecology I: The Earth System

9 weeks

4 hours/week

Ecology: from Cells to Gaia

4 weeks

2-3 hours/week

Ecology II: Engineering for Sustainability

9 weeks

4 hours/week

Ecology: Ecosystem Dynamics and Conservation

2 weeks

6 hours/week


Differential Equations I

Courses

Duration

Effort

Introduction to Differential Equations

15 weeks

5-8 hours/week

Differential Equations: 2x2 Systems

9 weeks

2-5 hours/week


Environmental Physics and Chemistry

Courses

Duration

Effort

The Chemistry of Life

13 weeks

2-3 hours/week

The Physics of Energy

9 weeks

5 hours/week

Physics and Chemistry of the Terrestrial Planets

9 weeks

5 hours/week

Climate Physics and Chemistry

9 weeks

5 hours/week

Atmospheric Chemistry

9 weeks

5 hours/week

Experimental Atmospheric Chemistry

9 weeks

5 hours/week

Aquatic Chemistry

9 weeks

5 hours/week

Fluid Mechanics

Courses

Duration

Effort

Thermodynamics & Kinetics

9 weeks

5 hours/week

Hydrodynamics

9 weeks

5 hours/week

Advanced Fluid Dynamics of the Environment

9 weeks

5 hours/week


Data Analysis

Courses

Duration

Effort

Data Science Professional Certificate

18 weeks

9 hours/week


Differential Equations II


Transport Processes

Courses

Duration

Effort

Transport Processes in the Environment

9 weeks

5 hours/week

Chemicals in the Environment: Fate and Transport

9 weeks

5 hours/week


Hydrology

Courses

Duration

Effort

Groundwater Hydrology

9 weeks

5 hours/week


Probability and Statistics

Courses

Duration

Effort

Fundamentals of Statistics

16 weeks

10-14 hours/week

Probability - The Science of Uncertainty and Data

16 weeks

10-14 hours/week


Modelling and Simulation

Courses

Duration

Effort

Mathematical Modelling Basics

9 weeks

4-8 hours/week

Simulation and modeling of natural processes

5 weeks

6 hours/week

Modeling Climate Change

8 weeks

3-5 hours/week


🎓 Specializations (Choose Your Path)

Once you've completed the core curriculum, explore a specialization aligned with your passion or career goals:

  • Water Resources & Management Focus on hydrological engineering, sustainable irrigation, and global water policy.

  • Sustainable Energy Systems Dive into renewable energy technologies, lifecycle analysis, and environmental impact assessments.

  • Environmental Data Science Learn geospatial analysis, machine learning for sustainability, and climate data visualization.

  • Urban Sustainability & Policy Explore how cities in 2025 are adapting through green infrastructure, policy reform, and environmental justice.

This curriculum is open-source and always in progress. Whether you’re a learner, practitioner, or educator, your feedback and contributions help improve the learning journey for others.

Learn. Share. Evolve. Welcome to the future of open environmental science education.