The rapid emergence of generative AI tools is transforming the way software is developed. Consequently, software engineering education must adapt to ensure that students not only learn traditional development methods but also understand how to meaningfully and responsibly use these new technologies. In particular, project-based courses offer an effective environment to explore and evaluate the integration of AI assistance into real-world development practices. This paper presents our approach and a user study conducted within a university programming project in which students collaboratively developed computer games. The study investigates how participants used generative AI tools throughout different phases of the software development process, identifies the types of tasks where such tools were most effective, and analyzes the challenges students encountered. Building on these insights, we further examine a repository-aware, locally deployed large language model (LLM) assistant designed to provide project-contextualized support. The system employs Retrieval-Augmented Generation (RAG) to ground responses in relevant documentation and source code, enabling qualitative analysis of model behavior, parameter sensitivity, and common failure modes. The findings deepen our understanding of context-aware AI support in educational software projects and inform future integration of AI-based assistance into software engineering curricula.

Generative AI (GenAI) is rapidly entering computer science education, yet its effects on student learning, skill development, and perceptions remain underexplored. Concerns about overreliance coexist with a gap in research on structured scaffolding to guide tool use in formal courses. This study examines the impact of a dedicated "AI-Lab" intervention -- emphasizing guided scaffolding and mindful engagement -- on undergraduate students in Data Structures and Algorithms, Competitive Programming, and first-year engineering courses at Purdue University. Over three semesters, we integrated AI-Lab modules into four mandatory and elective courses, yielding 831 matched pre- and post-intervention survey responses, alongside focus group discussions. Employing a mixed-methods approach, we analyzed quantitative shifts in usage patterns and attitudes as well as qualitative narratives of student experiences. While the overall frequency of GenAI usage for homework or programming projects remained largely stable, we observed large effect sizes in comfort and openness across conceptual, debugging, and homework problems. Notably, usage patterns for debugging also shifted statistically significantly, reflecting students' more mindful and deliberate approach. Focus group discussions corroborated these results, suggesting that the intervention "bridged the gap" between naive GenAI usage and more nuanced, reflective integration of AI tools into coursework, ultimately heightening students' awareness of their own skill development. These findings suggest that structured, scaffolded interventions can enable students to harness GenAI's benefits without undermining essential competencies. We offer evidence-based recommendations for educators seeking to integrate GenAI responsibly into computing curricula and identify avenues for future research on GenAI-supported pedagogy.

Many AI courses include design and programming projects that provide students with opportunities for experiential learning. Design and programming projects in courses on knowledge-based AI typically explore topics in knowledge, memory, reasoning, and learning. Traditional AI curricula, however, seldom highlight issues of modality of representations, often focusing solely on propositional representations. In this paper, we report on an investigation into learning about representational modality through a series of projects based around geometric analogy problems similar to the Raven's Progressive Matrices test of intelligence. We conducted this experiment over three years, from Fall 2010 through Fall 2012, in a class on knowledge-based AI. We used the methodology of action research in which the teacher is also the researcher. We discovered that students found these projects motivating, engaging, and challenging, in several cases investing significant time and posting their work online. From our perspective, the projects accomplished the goal of learning about representational modality in addition to knowledge representation and reasoning.

GPT-3 IS QUITE a beast. The Generative Pre-Trained Transformer 3, to give its full name, is a language model developed by OpenAI, a part-commercial, part not-for-profit artificial-intelligence (AI) laboratory in San Francisco. GPT-3 was trained on an unprecedented mass of text to teach it the probability that a given word will follow preceding words. When fed a short text "prompt", it cranks out astonishingly coherent prose written in a similar style. Your browser does not support the audio element.

GPT-3 IS quite a beast. The Generative Pre-Trained Transformer 3, to give its full name, is a language model developed by OpenAI, a part-commercial, part not-for-profit artificial-intelligence (AI) laboratory in San Francisco. GPT-3 was trained on an unprecedented mass of text to teach it the probability that a given word will follow preceding words. When fed a short text "prompt", it cranks out astonishingly coherent prose written in a similar style. Access to GPT-3 is restricted.

Please check Piazza regularly for announcements and discussion. I will attempt to post slides before lecture. Readings in CIML are required. Other readings are optional unless otherwise specified. Domingos, Pedro Domingos' video lectures on Coursera There are many excellent machine learning textbooks, but none of them is quite perfect for this class.

"One of the biggest problems in AT programming is the difficulty of specifying control. Meta-level architecture is a knowledge engineering approach to coping with this difficulty. The key feature of the architecture is a declarative control language that allows one to write partial specifications of program behavior. This flexibility facilitates incremental system dcvclopment and the integration of disparate architectures like demons, object-oriented programming, and controlled deduction. This paper presents the language, describes an appropriate, and cliscusses the issues of compiling. It illustrales the architecture with a variety of examples and reports some experience in using the architecture in building expert systems."Earlier: M. Genesereth and D.E. Smith. Meta-level Architecture. Memo HPP-81-6, Computer Science Department, Stanford University, 1981.In Proceedings of the AAAI, Washington, DC., August, 1983