We report on a debugging intervention introduced in the Cloud Native course---part of the AI Technicians program, a collaboration between the U.S. Army's AI Integration Center (AI2C) and Carnegie Mellon University. Learners take an introductory programming course prior to Cloud Native, yet their debugging skills did not transfer to this more advanced context. Across two cohorts, students consistently struggled to identify root causes of issues they faced. We describe the intervention we designed to address this gap and the instruments we are using to evaluate its impact.

The accelerating pace of developments in Artificial Intelligence (AI) and the increasing role that technology plays in society necessitates substantial changes in the structure of the workforce. Besides scientists and engineers, there is a need for a very large workforce of competent AI technicians (i.e., maintainers, integrators) and users (i.e., operators). As traditional 4-year and 2-year degree-based education cannot fill this quickly opening gap, alternative training methods have to be developed. We present the results of the first four years of the AI Technicians program which is a unique collaboration between the U.S. Army's Artificial Intelligence Integration Center (AI2C) and Carnegie Mellon University to design, implement and evaluate novel rapid occupational training methods to create a competitive AI workforce at the technicians level. Through this multi-year effort we have already trained 59 AI Technicians. A key observation is that ongoing frequent updates to the training are necessary as the adoption of AI in the U.S. Army and within the society at large is evolving rapidly. A tight collaboration among the stakeholders from the army and the university is essential for successful development and maintenance of the training for the evolving role. Our findings can be leveraged by large organizations that face the challenge of developing a competent AI workforce as well as educators and researchers engaged in solving the challenge.

Self-efficacy has been claimed to be a predictor of students' motivation and learning [1]. It has been found to be sensitive to students' success, and to affect their academic achievement. In the CS/IT education context, where the drop rates are high, it is important that students not only gain knowledge and skills, but also self-efficacy, so that they persist in the program. In this study, we investigate 602 students taking an introductory Python course via different delivery methods: (i) traditional in-person; (ii) cohort in-person; (iii) synchronous online; and (iv) asynchronous online. Although modality predicted retention and success, we found no apparent links among learning, student retention, and self-efficacy. However we found evidence that cohort learning may in particular help struggling students catch up with their peers.

As Artificial Intelligence (AI) continues to make its presence felt in transforming workplaces around the world, and the Information Technology industry in particular, it is essential to understand its impact on the work practices of IT professionals, and the implications for computing students and curricula. This research project builds on work initiated jointly, in Sweden, New Zealand and Scotland, investigating concerns about the increasing impacts of Artificial Intelligence in IT Sector workplaces for employee work engagement and the implications for tertiary study, assessment and curricula in computing. "Work engagement", has been defined as the positive inner state where employees are fully present and engaged in their work, and is closely linked to motivation, learning, productivity, and accountability. Within the context of (Generative) AI at work, IT professionals have been noted as early adopters of AI. Their involvement in implementing and utilising AI technologies can provide valuable insights into the interplay between AI and work engagement. The implications for students are significant as future IT professionals, who must acquire and enhance competencies to adapt and thrive in digital workplaces.

In this poster we propose an approach to designing auto-graded programming course projects that are modular and easily manageable by an instructor. Based on our experiences with the Sail() platform which supports auto-grading and feedback generation in multiple contexts, we design the approach to overcome the challenges we observed. The approach is especially focused on designing projects that can be utilized by multiple instructors who may have various scopes or students with varying backgrounds. The approach enables differentiated learning-thereby improving learning experiences and outcomes. We also discuss challenges of using such a modular approach to auto-graded projects.

Knowledge and experience from working in international collaborative projects prepares engineering students for their future careers in a global market. Hence this is an important element in higher education, although seldom provided in the curriculum. One reason for this lack might be uncertainty in how to manage international collaborations and to create a good learning environment for the development of skills related to international collaborations. Therefore this article describes our experiences from managing international student collaborations including theoretical underpinning for our choices when relevant. Having given the context of, and the strategies for running our two collaborations, we provide an analysis of our experiences. This includes observations of differences in the two collaborations, both when due to use of different strategies and when due to different contexts, as well as observations of similarities. With this in mind, we present recommendations for running international student collaborations.

In this poster, it is aimed to share the experience of using course management systems (CMS) in a CS-1 course. The aim was to focus on the individual needs of students while learning programming. Creating a social online environment for solving exercises and sharing solutions seemed to be effective to support instructors as well as students.

In this poster, we first aim to explain an interdisciplinary approach and question an idea and attempt. Second, we aim to underline challenges and enablers of such an attempt. This idea can be briefly summarized as "inclusion of learning activities and assessments that are applied in coordination with the Department of Fine Arts might be necessary in terms of developing visual design skills". The target of this approach is improving the courses like 'human computer interaction' or 'user-interface design'.