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Case Study: Using ChatGPT for Concept Mastery in Thermodynamics

Course Context

Course: Engineering Thermodynamics II (ME 2304)
Level: Sophomore
Enrollment: 110 students
Prior Format: Lecture-based with weekly problem sets and exams
Tools: Equation solvers, property tables, simulation software
Faculty: Dr. B, Associate Professor of Mechanical Engineering

Implementation Challenge

Dr. B identified several challenges in teaching thermodynamics that AI assistance could potentially address:

Conceptual Difficulty: Students struggle with abstract thermodynamic concepts
Problem-Solving Approach: Difficulty in developing systematic problem-solving strategies
Mathematical Barriers: Varying levels of mathematical preparation impeding understanding
Misconception Persistence: Common thermodynamic misconceptions resistant to traditional teaching
Individualized Support: Limited office hours for a large enrollment course

Implementation Goals

The integration of ChatGPT aimed to:

Provide on-demand conceptual explanations tailored to individual understanding levels
Help develop systematic problem-solving approaches for thermodynamic cycles
Bridge mathematical gaps without detracting from thermodynamic principles
Address common misconceptions with multiple explanation strategies
Extend instructional support beyond classroom and office hours

Implementation Process

Phase 1: Faculty Preparation (Winter break)

Tool Exploration:
- Tested ChatGPT's understanding of thermodynamic principles
- Identified strengths in explanation and weaknesses in calculation
- Developed prompts for common thermodynamic concepts
- Created problem-solving templates for different thermodynamic systems
- Documented common misconceptions and effective correction strategies
Content Integration Planning:
- Identified key concepts for AI-enhanced explanation
- Developed scaffolded problem-solving templates
- Created conceptual checkpoints for self-assessment
- Designed workflow integrating AI support with course materials
- Established verification protocols for technical accuracy
Assessment Adaptation:
- Redesigned homework to include AI-assisted and independent components
- Created reflection prompts about conceptual understanding
- Developed guidelines for appropriate AI use in coursework
- Designed new assessment approaches focusing on conceptual understanding
- Created rubrics valuing process explanation over final answers

Phase 2: Initial Implementation (First three weeks)

Student Introduction:
- Conducted 75-minute workshop on effective ChatGPT use for thermodynamics
- Demonstrated example prompts for different learning scenarios
- Established guidelines for appropriate AI assistance
- Practiced critical evaluation of AI-generated explanations
- Emphasized verification of calculations and principles
Guided Integration:
- Assigned specific ChatGPT practice exercises with reflection
- Provided feedback on students' prompt strategies
- Facilitated small-group comparison of different explanation approaches
- Discussed how to recognize and address AI misconceptions
- Gathered early feedback on perceived helpfulness
Progressive Framework:
- Started with highly structured prompts and templates
- Gradually reduced scaffolding as student proficiency increased
- Developed class-sourced best practices for different concept areas
- Created repository of effective prompts for different learning needs
- Established peer support system for AI-enhanced studying

Phase 3: Full Implementation (Throughout semester)

Concept Mastery Support:
- Students used ChatGPT to explore alternative explanations for difficult concepts
- Created concept maps with AI assistance to connect thermodynamic principles
- Developed analogies and visualizations to reinforce understanding
- Used AI to generate conceptual questions testing deeper understanding
- Documented personal conceptual breakthroughs facilitated by AI explanations
Problem-Solving Integration:
- AI served as problem-solving coach rather than solution provider
- Students documented their problem-solving process with AI guidance
- Used ChatGPT to analyze incorrect approaches and identify conceptual gaps
- Practiced breaking complex problems into conceptual components
- Compared AI approaches with textbook methods to develop critical thinking
Metacognitive Development:
- Students maintained learning journals documenting their conceptual evolution
- Reflected on how different explanation strategies affected their understanding
- Identified personal learning preferences through varied AI approaches
- Developed self-assessment skills by checking understanding with new scenarios
- Created personalized study guides with targeted explanations

Implementation Examples

Example 1: Conceptual Explanation Chains

Traditional Challenge: Students often memorized equations without understanding the underlying principles, leading to difficulties when facing novel problems.

ChatGPT-Enhanced Approach: Students used a series of progressively deeper prompts to build conceptual understanding of entropy.

Prompt Sequence Example:

Initial Prompt:
"Explain the concept of entropy in thermodynamics at a sophomore engineering level."

Follow-up Prompt:
"I'm struggling to understand how entropy relates to available energy. Can you explain 
the connection between entropy, availability, and irreversibility using a simple example 
of a heat engine?"

Deepening Prompt:
"I think I understand the basics now. Can you help me understand how to determine if a 
process will occur spontaneously by analyzing entropy generation? Please use the example 
of heat transfer between two bodies at different temperatures."

Application Prompt:
"Now I'd like to apply this understanding to a refrigeration cycle. How does entropy 
help us analyze the performance and limitations of a vapor-compression refrigeration system?"

Learning Enhancement: Students created personal "explanation chains" for difficult concepts, documenting how their understanding evolved through multiple explanation approaches.

Example 2: Problem-Solving Coach

Traditional Challenge: Students often jumped directly to equations without systematic problem analysis, leading to errors and confusion with complex thermodynamic cycles.

ChatGPT-Enhanced Coaching: Instead of seeking solutions, students used ChatGPT as a Socratic coach to develop systematic problem-solving approaches.

Problem-Solving Template:

I'm working on solving a problem about a Rankine cycle with reheat. Rather than solving 
it for me, I'd like you to act as a coach who helps guide my problem-solving process.

Here's the problem: [student inserts problem statement]

Please help me by:
1. Asking me questions about how I should approach this systematically
2. Guiding me to identify the key states in the cycle I need to analyze
3. Helping me recall relevant principles and equations without simply stating them
4. Pointing out potential conceptual misunderstandings in my approach
5. Suggesting how to verify my results for reasonableness

I'll share my thinking at each step, and you can provide guidance to keep me on track.

Implementation in Assignments: Weekly problem sets included a "process portfolio" component where students documented their problem-solving approach, including:

Initial problem analysis
AI coaching interactions
Key decision points and conceptual realizations
Verification of results
Reflection on approach effectiveness

Example 3: Misconception Confrontation

Traditional Challenge: Common thermodynamic misconceptions often persisted despite traditional instruction.

ChatGPT-Enhanced Approach: Students actively engaged with misconceptions through structured exploration.

Misconception Analysis Assignment:

For this assignment, you will explore common misconceptions in thermodynamics using ChatGPT as a learning tool:

Select two misconceptions from the provided list that you find personally challenging

For each misconception:

Generate an explanation of why the misconception seems intuitively correct

Ask ChatGPT to provide multiple perspectives challenging this misconception

Request real-world examples that clearly demonstrate why the misconception is false

Develop a conceptual test that would help someone determine if they still hold the misconception

Create a brief (2-minute) explanation video of each misconception and its correction that could help fellow students

Reflect on how your understanding changed through this process

Misconception Prompt Example:

I'm trying to deeply understand a common misconception in thermodynamics: "Heat and 
temperature are the same thing."

1. First, explain why this misconception seems intuitively reasonable to many students.

2. Then, provide three different explanations (using different approaches or analogies) 
   for why heat and temperature are distinct concepts.

3. Give me a concrete example that clearly demonstrates the difference between heat and 
   temperature in a way that's impossible to confuse.

4. Suggest a simple experiment or thought exercise I could use to test whether someone 
   truly understands the distinction.

Assessment Strategy

Evolving Assessment Approach

Before AI Integration: Traditional Assessment

Weekly problem sets focusing on numerical solutions (40%)
Midterm and final exams with calculation-heavy problems (50%)
Participation and quizzes (10%)

After AI Integration: Concept-Focused Assessment

Weekly problem sets with both process documentation and solutions (25%)
Concept mapping and misconception analysis assignments (15%)
Explanation development assignments (15%)
Modified exams with conceptual and application focus (35%)
Learning portfolio documenting conceptual evolution (10%)

New Assessment Components

Explanation Quality Evaluation:
- Students' ability to generate clear explanations of thermodynamic concepts
- Multiple representation approaches (verbal, mathematical, graphical)
- Ability to tailor explanations to different knowledge levels
- Recognition of conceptual connections between topics
Problem-Solving Process Documentation:
- Systematic approach to thermodynamic problems
- Identification of relevant principles before equation application
- Clear state identification and assumption justification
- Reasonableness checks and result verification
- Reflection on alternative approaches
Conceptual Understanding Demonstrations:
- Creation of novel examples illustrating thermodynamic principles
- Development of conceptual questions testing understanding
- Identification of potential pitfalls or misconceptions
- Ability to transfer concepts to unfamiliar scenarios

Potential Outcomes and Considerations

Expected Benefits

Deeper conceptual understanding beyond equation manipulation
More systematic and principled problem-solving approaches
Greater student autonomy in learning and self-assessment
Increased engagement with theoretical foundations
Better preparation for advanced thermodynamics applications
Stronger connections between mathematics and physical principles
Development of explanation skills valuable for engineering communication

Potential Challenges

Risk of over-reliance on AI for conceptual understanding
Technical errors in AI-generated thermodynamic explanations
Difficulty ensuring students engage deeply rather than superficially
Additional time required for process documentation
Balancing conceptual focus with necessary quantitative skills
Managing varying levels of AI proficiency among students

Faculty Implementation Considerations

Key Implementation Strategies

Explanation before calculation emphasis in all aspects of the course
Multiple representation requirement for concept demonstrations
Process documentation as a core component of assessment
Guided prompt development to ensure effective AI interactions
Verification protocols for ensuring conceptual and technical accuracy

Important Considerations

Clear boundaries for appropriate AI use in different assessment contexts
Technical fundamentals must still be mastered independently
Conceptual integration should be emphasized over isolated topics
Metacognitive development is as important as content mastery
Progressive autonomy in AI interaction supports long-term learning

Future Refinement Directions

If implementing such an approach, consider:

Developing discipline-specific prompt libraries for thermodynamic concepts
Creating a structured misconception database with effective correction strategies
Implementing peer-teaching components using AI-enhanced explanations
Adding more real-world applications to reinforce conceptual understanding
Creating advanced metacognitive frameworks for self-directed learning

Resources Developed

Thermodynamics Concept Prompt Library: Structured prompts for key thermodynamic principles
Problem-Solving Templates: Frameworks for different thermodynamic systems and processes
Misconception Database: Common errors with multiple correction approaches
Process Documentation Templates: Structured formats for showing problem-solving thinking
Verification Checklists: Technical accuracy guides for different topic areas

Implementation Advice

For Faculty Considering Similar Integration:

Start with conceptually challenging topics where multiple explanations add value
Emphasize process over answers in all assessment components
Create clear documentation requirements to make thinking visible
Develop verification habits from the beginning of implementation
Build a progressive framework that gradually transfers responsibility to students

Pedagogical Considerations:

AI works best as a supplement to strong foundational instruction
Balance conceptual and quantitative components throughout
Encourage student-generated examples to deepen understanding
Use AI to bridge diverse learning preferences and preparation levels
Foster metacognition through regular reflection on learning processes

This case study was developed as part of the "Strategies for Integrating Generative AI in Engineering Education" workshop materials in collaboration with Claude-3.7 Sonnet.

Thermodynamics Concept Mastery with ChatGPT