Preparing Purdue Engineers to Use AI Thoughtfully and Responsibly
Artificial intelligence is changing how engineers analyze data, write code, design systems, communicate ideas, and make decisions. In Purdue First-Year Engineering, AI literacy is part of a broader curriculum redesign focused on technical rigor, computational and data fluency, professional responsibility, and preparedness for future engineering coursework.
First-Year Engineering (FYE) students learn that AI is part of modern engineering practice, but it must not be used as a black box. Students develop foundational understanding of how AI and machine learning systems work, apply AI tools within engineering workflows, evaluate and validate outputs, and communicate transparently about AI-assisted decisions.
Aligned with Purdue's AI Literacy FLO
Purdue's AI Literacy Foundational Learning Outcome asks students to understand, use, evaluate, and communicate about artificial intelligence. First-Year Engineering satisfies this requirement across its course pathways by addressing key AI literacy skills, including tool use and comparison, recognition of strengths and limitations, ethical reasoning, and clear communication about AI use.
FYE extends the University-level FLO into an engineering-specific framework organized around four outcomes:
- Applied and comparative AI use
- Conceptual understanding and algorithmic reasoning
- Critical evaluation and validation
- Ethical and professional communication
After Completing FYE, Students Will Be Able To:
- Explain foundational AI and machine learning concepts, including digital logic, decision trees, regression, neural networks, large language models, and diffusion models.
- Build and train introductory machine learning models using accessible tools such as spreadsheets, with progression into Python and MATLAB.
- Apply generative AI tools to engineering design tasks, including problem scoping, ideation, technical problem-solving, and visual communication.
- Evaluate and compare AI-assisted outputs, tools, and models by recognizing strengths, limitations, failure modes, and appropriate engineering use cases.
- Communicate transparently about AI use, including tool selection, documentation, validation, and ethical considerations.
Examples Across FYE Courses
ENGR 131/132: Foundations, Modeling, and Responsible Generative AI Use
Students learn foundational AI and machine learning concepts, build introductory models using spreadsheets, and apply generative AI tools such as ChatGPT, Claude, or Gemini to engineering design tasks including problem scoping and ideation. In ENGR 132, students extend these concepts into programming, algorithmic thinking, optimization, and applied engineering decision-making using MATLAB and Python.
ENGR 133: Applied Machine Learning and Image Classification
Students complete an applied machine-learning project connected to self-driving vehicle systems. They preprocess traffic sign images, extract features, create training/validation/test data sets, apply models such as K-nearest neighbors, logistic regression, and decision trees, evaluate model performance, and communicate results through demonstrations and technical reporting.
ENGR 130: AI in Sensing, Coding, Design, and Communication
Accelerated students engage with AI through integrated activities in design, programming, robotics, sensing, and systems thinking. Projects include TinyML and edge-computing activities, sensor-data collection and processing, machine-learning model training and evaluation, and generative AI-supported coding in Python or MATLAB. Students document AI tool use, validation, and ethical or bias considerations in oral and written reports.
Our Approach
AI can support engineering work, but it does not replace engineering judgment. FYE prepares students to use AI as thoughtful, responsible, and technically grounded emerging engineers.