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

Allocating students to projects is a commonplace task in computing education. These decisions underpin student-supervisor allocation, the formation of tutee and capstone groups, and pair programming. These allocations play a critical role for individual learner outcomes and the success of collaborative interventions. For example, imbalance in either gender, ethnicity, or nationality can negatively impact learner outcomes. Despite the critical importance of these allocation choices, we see little consensus on how these are implemented. The allocation task can be challenging and time-consuming for instructors of even moderately-sized classes, and the fairness implications can be difficult to assess. Inadvertently, an instructor may allocate in a way that amplifies existing biases or disproportionately harms those from disadvantaged or protected groups. From students' perspectives, a lack of transparency on the allocation process may also lead to issues of trust. The Working Group will undertake a study of allocation practices by bringing together educational and ML literature to develop and evaluate the fairness of allocation methods, and develop educator guidelines to promote pedagogically grounded allocation practices.

Faculty and industry practitioners recognize written communication to be important in computer science, but it can be challenging to convince students of the same. As student perceptions are molded early in a program of study, we focus on early-year CS students to understand their perceptions towards the importance of writing in CS, with the goal of framing discipline-specific writing pedagogy. We qualitatively analyze responses from first and second-year CS students in a survey about the role of writing in their field. The responses reveal that a majority view writing as an indispensable skill. Specifically, students recognize it as a fundamental skill, applicable across diverse contexts, and uniquely relevant in CS compared to other fields. We identified 4 perceptions that they hold which are helpful to their development as writers: that writing is a useful fundamental skill, which is useful for achieving various goals in a variety of contexts, and that writing in CS is different than in other fields. However, 20% of responses include reasons why writing is not important in CS, and we identify 4 perceptions harmful to students' development as writers: that writing skills can be avoided, are defined narrowly, do not need to be developed beyond a baseline, and come at the cost of computing skills. We believe that there is an opportunity to align discipline-specific writing instruction with these useful and harmful perceptions.

One of the less explored approaches to foster equity, diversity, and inclusion (EDI) in Computer Science (CS) is through changes to the curriculum. Despite sporadic work on the adoption of Culturally Responsive Computing (CRC) and Universal Design for Learning (UDL), the inclusion of equity-minded courses, or modifications on specific elements of the curriculum such as introductory programming courses, there has never been a wide exploration or adoption of a successful equity-minded undergraduate CS curriculum.

Most active learning methods aim to engage students in collaborative problem-solving. While active learning and collaboration benefits are indisputable, more investigation is needed to understand student engagement in group activities. This qualitative study investigates the student perspective on group work in a CS2 inverted classroom, to better understand the learner mindset and identify potential barriers or conduits for collaborative engagement. We conducted 30-45 minute interviews with 30 participants from six sections of CS2, with five sections being scheduled in an Active Learning Classroom (ALC) and one in a traditional lecture hall, all taught in the same inverted model and using the same in-class activities. A multitude of facets of student behavior or engagement in group work and interactions with peers were identified via emergent coding. We classified emerging themes into higher-order categories which subsume semantically-related themes, forming a hierarchy with the top-level categories being Perceived Utility and Social Environment. This classification is intended to provide insight to educators seeking to better engage students in active learning via collaborative in-class activities.

Active learning environments have only recently started to be analyzed in the CS discipline, in terms of their effect on student performance. Recent studies in CS1 found contradictory results, in part due to different control on the learning pedagogy used, and issued a call for further investigation. This study evaluates the effects of the learning space on student performance in CS2, as measured by their grades. We use a quasi-experimental setup with 529 participants across five lecture sections over one academic term. All sections employ the same active learning method (inverted classroom), identical lecture materials, and the same number of TAs for in-class support, but differ in terms of classroom type (active learning classroom vs traditional lecture hall), instructor, and lecture time of day. Similarly to a recent study in CS1, we find no significant impact of the learning space in CS2. We also inspect factors not analyzed in previous studies, such as student prior preparation (as measured by prerequisite CS1 grades), course drop rates, and exam failure rates, and find that the CS2 sections are statistically similar. This work also examines student survey responses, to assess student perception differences on properties of the learning space which may impact their learning experience, such as the use of technology, ability to hear the instructor, ability to get help during lectures, and conduciveness of desk types to group work.