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

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.

Cold calling effectively incentivizes all students to actively prepare contributions to a class discussion, but some find it terrifying. Rewarding voluntarily speaking in class is less off-putting, and can be valuable for students who participate; however, it can allow a large fraction of the class to disengage. Agora is an open-source app designed to serve as a middle ground between these extremes, with the added benefit that it automatically produces an assessment of each student's engagement. The key ideas are to give students control over whether their hand is raised or lowered, to choose randomly among students with raised hands, and to give participation credit to all students who were considered every time a speaker is chosen. The system has various other features to facilitate deployment in large classes including multiple queues to support concurrent questions on different topics; a message board to allow students to communicate discretely with the instructor; and polling. We deployed the system in three offerings of a large undergraduate class and demonstrate its effectiveness in terms of learning outcomes, gender balance in participation, and student satisfaction.

Computing science education has experienced low attendance and historic declines in registration from different minority groups. The past decade of enrollment surge in computer science undergraduate programs has increased the number of women and minorities in the field, but the improvements are inconsistent and less than expected. An increase in the use of computing science and in the demand of technology workforce is expected in the upcoming years. Thus, computing science is set to shape the future of technology for a diverse set of technology users. Therefore, it is important to analyze how undergraduate program admission procedures are affecting Equity, Diversity, and Inclusion of historically marginalized groups in computing science.

Mastery Learning, a pedagogical strategy in which students are allowed to prove mastery of the skills acquired in a course over multiple attempts (and used failed attempts as feedback) is becoming increasingly popular in higher education. Large introductory programming courses can use it to strengthen students' preparation for later courses, but some challenges to its adoption remain, such as how to scale this format to hundreds of students, or how to ensure that students do not fall behind on the material. In Spring 2021, the instructors at the Anonymous University transformed the structure of their CS1 course using a Mastery Learning format, reorganizing the material in units focused on the different course topics. Students were allowed to prove mastery of each unit separately and over multiple times, without penalties for missed or failed attempts. In this experience report, we will describe the strategies adopted to cater to a large cohort of novice students. We will compare the students' learning experience with a cohort of students who took the course in a more traditional format, and show that the students benefited from the new format in terms of quantity of skills mastered. Students also exhibited signs of increased motivation to practice and complete tests without grade incentives. Finally, we will discuss some pitfalls in our design and address some of the concerns of instructors interested in trying a Mastery Learning approach in their CS1 courses.

The field of computer science has a problem of representation - many groups are not represented in our classroom at levels approaching their composition in society. Unfortunately, the representation issue is a larger societal issue and begins well before students enter our institutions. Though we acknowledge that building inclusive and equitable classroom environments cannot increase representation by itself, it can have an impact on retention and inclusion for members of marginalized communities.

Objective: Unlocking the data contained within both structured and unstructured components of electronic health records (EHRs) has the potential to provide a step change in data available for secondary research use, generation of actionable medical insights, hospital management, and trial recruitment. To achieve this, we implemented SemEHR, an open source semantic search and analytics tool for EHRs. Methods: SemEHR implements a generic information extraction (IE) and retrieval infrastructure by identifying contextualized mentions of a wide range of biomedical concepts within EHRs. Natural language processing annotations are further assembled at the patient level and extended with EHR-specific knowledge to generate a timeline for each patient. The semantic data are serviced via ontology-based search and analytics interfaces. Results: SemEHR has been deployed at a number of UK hospitals, including the Clinical Record Interactive Search, an anonymized replica of the EHR of the UK South London and Maudsley National Health Service Foundation Trust, one of Europe's largest providers of mental health services. In 2 Clinical Record Interactive Search-based studies, SemEHR achieved 93% (hepatitis C) and 99% (HIV) F-measure results in identifying true positive patients. At King's College Hospital in London, as part of the CogStack program (github.com/cogstack), SemEHR is being used to recruit patients into the UK Department of Health 100 000 Genomes Project (genomicsengland.co.uk). The validation study suggests that the tool can validate previously recruited cases and is very fast at searching phenotypes; time for recruitment criteria checking was reduced from days to minutes. Validated on open intensive care EHR data, Medical Information Mart for Intensive Care III, the vital signs extracted by SemEHR can achieve around 97% accuracy. Conclusion: Results from the multiple case studies demonstrate SemEHR's efficiency: weeks or months of work can be done within hours or minutes in some cases. SemEHR provides a more comprehensive view of patients, bringing in more and unexpected insight compared to study-oriented bespoke IE systems. SemEHR is open source, available at https://github.com/CogStack/SemEHR.