The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of thirteen AI assignments from the 2025 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu

The Model AI Assignments session seeks to gather and dis- seminate the best assignment designs of the Artificial In- telligence (AI) Education community. Recognizing that as- signments form the core of student learning experience, we here present abstracts of five AI assignments from the 2024 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment spec- ifications and supporting resources may be found at http://modelai.gettysburg.edu.

In this paper, we demonstrate a novel technique for dynamically generating an emotionally-directed video game soundtrack. We begin with a human Conductor observing gameplay and directing associated emotions that would enhance the observed gameplay experience. We apply supervised learning to data sampled from synchronized input gameplay features and Conductor output emotional direction features in order to fit a mathematical model to the Conductor's emotional direction. Then, during gameplay, the emotional direction model maps gameplay state input to emotional direction output, which is then input to a music generation module that dynamically generates emotionally-relevant music during gameplay. Our empirical study suggests that random forests serve well for modeling the Conductor for our two experimental game genres.

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of six AI assignments from the 2023 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu .

In this paper, we introduce a technique for AI generation of the Bullets puzzle, a paper-and-pencil variant of Minesweeper. Whereas traditional Minesweeper can be lost due to the need to guess mine or non-mine positions, our puzzle is fully deducible from a minimal clue set. Puzzle generation is based on analysis and optimization of solutions from a human-like reasoning engine that classifies types of deductions. Additionally, we provide insights to subjective puzzle quality, minimal clue sampling trade-offs, and optimal bullet density.

In this article, we describe various approaches to opponent hand estimation in the card game Gin Rummy. We use an application of Bayes' rule, as well as both simple and convolutional neural networks, to recognize patterns in simulated game play and predict the opponent's hand. We also present a new minimal-sized construction for using arrays to pre-populate hand representation images. Finally, we define various metrics for evaluating estimations, and evaluate the strengths of our different estimations at different stages of the game.

We perform an empirical study of Gin Rummy knocking strategies, drawing insight from a population of AI players that vary in both discarding and knocking strategies. For our best performing player, simple linear regression yielded a knocking strategy that both affirmed the features expert players give attention to in making knock decisions, and yet called into question the way such features are conventionally used.

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of six AI assignments from the 2021 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.

This paper describes a deterministic approach to building a fixed-strategy gin rummy player. In the paper, we develop and evaluate both heuristic and neural network models for informing draw, discard, and knock decisions in the game. In this empirical study, we test performance of the models through competitive game play, show which best inform strategy, and demonstrate statistical significance of the improvement over a simple strategy. Through this empirical study, we indicate features that we expect to be helpful in future improvements to Gin Rummy play.

We develop a data-driven approach for hand strength evaluation in the game of Gin Rummy. Employing Convolutional Neural Networks, Monte Carlo simulation, and Bayesian reasoning, we compute both offensive and defensive scores of a game state. After only one training cycle, the model was able to make sophisticated and human-like decisions with a 55.4% +/- 0.8% win rate (90% confidence level) against a Simple player.

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of nine AI assignments from the 2020 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.

In this article, we describe the lessons learned in creating an efficient solver for the solitaire game Birds of a Feather. We introduce a new variant of depth-first search that we call best-n depth-first search that achieved a 99.56% reduction in search time over 100,000 puzzle seeds. We evaluate a number of potential node-ordering search features and pruning tests, perform an analysis of solvability prediction with such search features, and consider possible future research directions suggested by the most computationally expensive puzzle seeds encountered in our testing.

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of ten AI assignments from the 2019 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http: //modelai.gettysburg.edu.

In this article, we describe a computer-aided design process for generating high-quality Birds of a Feather solitaire card puzzles. In each iteration, we generate puzzles via combinatorial optimization of an objective function. After solving and subjectively rating such puzzles, we compute objective puzzle features and regress our ratings onto such features to provide insight for objective function improvements. Through this iterative improvement process, we demonstrate the importance of the halfway solvability ratio in quality puzzle design. We relate our observations to recent work on tension in puzzle design, and suggest next steps for more efficient puzzle generation.

We describe the experimental development of an AI player that adapts to different point systems for Parameterized Poker Squares. After introducing the game and research competition challenge, we describe our static board evaluation utilizing learned evaluations of abstract partial Poker hands. Next, we evaluate various time management strategies and search algorithms. Finally, we show experimentally which of our design decisions most signicantly accounted for observed performance.

The field of AI has changed significantly in the past couple of years and will likely continue to do so. Driven by a desire to expose our students to relevant and modern materials, we conducted two surveys, one of AI instructors and one of AI practitioners. The surveys were aimed at gathering infor-mation about the current state of the art of introducing AI as well as gathering input from practitioners in the field on techniques used in practice. In this paper, we present and briefly discuss the responses to those two surveys.

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of five AI assignments from the 2014 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.

In 2010 a new annual symposium on Educational Advances in Artificial Intelligence (EAAI) was launched as part of the AAAI annual meeting. The event was held in cooperation with ACM SIGCSE and has many similar goals related to broadening and disseminating work in computer science education. EAAI has a particular focus, however, as the event is specific to educational work in Artificial Intelligence and collocated with a major research conference (AAAI) to promote more interaction between researchers and educators in that domain. This panel seeks to introduce participants to EAAI as a way of fostering more interaction between educational communities in computing. Specifically, the panel will discuss the goals of EAAI, provide an overview of the kinds of work presented at the symposium, and identify potential synergies between that EAAI and SIGCSE as a way of better linking the two communities going forward.

Our approach to teaching introductory artificial intelligence (AI) unifies its diverse core topics through a theme of machine learning, and emphasizes how AI relates more broadly with computer science. Our work, funded by a grant from the National Science Foundation, involves the development, implementation, and testing of a suite of projects that can be closely integrated into a one-term AI course. Each project involves the development of a machine learning system in a specific application. These projects have been used in six different offerings over a three-year period at three different types of institutions. While we have presented a sample of the projects as well as limited preliminary experiences in other venues, this article presents the first assessment of our work over an extended period of three years. Results of assessment show that the projects were well received by the students. By using projects involving real-world applications we provided additional motivation for students. While illustrating core concepts, the projects introduced students to an important area in computer science, machine learning, thus motivating further study.

An introductory Artificial Intelligence (AI) course provides students with basic knowledge of the theory and practice of AI as a discipline concerned with the methodology and technology for solving problems that are difficult to solve by other means. It is generally recognized that an introductory Artificial Intelligence course is challenging to teach. This is, in part, due to the diverse and seemingly disconnected core AI topics that are typically covered. Recently, work has been done to address the diversity of topics covered in the course and to create a theme-based approach. Russell and Norvig present an agent-centered approach [9]. Others have been working to integrate Robotics into the AI course [1, 2, 3].We present work on a project funded by the National Science Foundation with a goal of unifying the artificial intelligence course around the theme of machine learning. This involves the development and testing of an adaptable framework for the presentation of core AI topics that emphasizes the relationship between AI and computer science. Machine learning is inherently connected with the AI core topics and provides methodology and technology to enhance real-world applications within many of these topics. Machine learning also provides a bridge between AI technology and modern software engineering. In his article, Mitchell discusses the increasingly important role that machine learning plays in the software world and identifies three important areas: data mining, difficult-to-program applications, and customized software applications [6].We have developed a suite of adaptable, hands-on laboratory projects that can be closely integrated into the introductory AI course. Each project involves the design and implementation of a learning system which will enhance a particular commonly-deployed application. The goal is to enhance the student learning experience in the introductory artificial intelligence course by (1) introducing machine learning elements into the AI course, (2) implementing a set of unifying machine learning laboratory projects to tie together the core AI topics, and (3) developing, applying, and testing an adaptable framework for the presentation of core AI topics which emphasizes the important relationship between AI and computer science in general, and software development in particular. Details on this project as well as samples of course materials developed are published in [4, 5, 7, 8] and are available at the project website at http://uhaweb.hartford.edu/compsci/ccli.We present an overview of our work along with a detailed presentation of one of these projects and how it meets our goals.The project involves the development of a learning system for web document classification. Students investigate the process of classifying hypertext documents, called tagging, and apply machine learning techniques and data mining tools for automatic tagging. Our experiences using the projects are also presented.