Graduate Teaching Assistants (GTAs) support a substantial share of undergraduate computing instruction, yet many enter their roles with limited preparation and opportunity for feedback. To create greater consistency and scalability, we implemented a redesigned GTA training at a large R1 university that integrates multiday workshops, asynchronous modules, and AI-simulated student communication practice. The model emphasizes professional communication, classroom management, and equitable teaching practices while generating diagnostic data to inform ongoing instructional coaching.

We present Owlgorithm, an educational platform that supports Self-Regulated Learning (SRL) in competitive programming (CP) through AI-generated reflective questions. Leveraging GPT-4o, Owlgorithm produces context-aware, metacognitive prompts tailored to individual student submissions. Integrated into a second- and third-year CP course, the system-provided reflective prompts adapted to student outcomes: guiding deeper conceptual insight for correct solutions and structured debugging for partial or failed ones. Our exploratory assessment of student ratings and TA feedback revealed both promising benefits and notable limitations. While many found the generated questions useful for reflection and debugging, concerns were raised about feedback accuracy and classroom usability. These results suggest advantages of LLM-supported reflection for novice programmers, though refinements are needed to ensure reliability and pedagogical value for advanced learners. From our experience, several key insights emerged: GenAI can effectively support structured reflection, but careful prompt design, dynamic adaptation, and usability improvements are critical to realizing their potential in education. We offer specific recommendations for educators using similar tools and outline next steps to enhance Owlgorithm's educational impact. The underlying framework may also generalize to other reflective learning contexts.

Contribution: This research-to-practice full paper presents “GAIDE: Generative AI for Instructional Development and Education,” introducing a pragmatic and systematic framework for employing Generative AI (GenAI) in the development of educational content. Unlike existing frameworks, GAIDE emphasizes practical applicability for educators, facilitating the creation of diverse, engaging, and academically sound materials. The novel aspect of our approach lies in its detailed methodology for integrating GenAI into curriculum design processes, thereby reducing instructors' workload and improving the quality of educational materials. Through GAIDE, we contribute a distinct, adaptable model for leveraging technological advancements in education, providing a foundational step towards more efficient and effective instructional material development. Background: The motivation for our study emerges from the increasing demand for innovative and engaging educational content, coupled with the notable rise in Generative AI (GenAI) utilization among students for academic tasks. Our investigations reveal that nearly half of students engage with GenAI tools for completing homework assignments, highlighting a significant shift in study behaviors and the potential for technology to shape educational practices. This scenario presents a dual challenge for educators: to adapt to and incorporate these emerging technologies into their teaching methodologies, not merely to keep pace with technological advancements but to leverage them in fostering a more dynamic and inclusive learning environment. This research addresses these challenges by offering a concrete, adaptable solution, aiming to reshape the landscape of educational content creation and its application across diverse learning settings. Intended Outcomes: The primary objectives of introducing GAIDE are to: 1) Streamline the course content development process for educators, 2) Foster the creation of dynamic, engaging, and varied educational materials, and 3) Demonstrate the practical utility of GenAI in enhancing instructional design, potentially setting a precedent for its adoption in diverse educational contexts. Application Design: GAIDE was conceived out of a necessity to efficiently harness GenAI's potential in education. The application design is rooted in constructivist learning theory and TPCK, emphasizing the importance of integrating technology in a manner that complements pedagogical goals and content knowledge. Our Outcomes-Based Course Design approach aids educators in crafting effective GenAI prompts and guides them through interactions with GenAI tools, both of which are critical for generating high-quality, contextually appropriate content. Findings: Preliminary evaluation of GAIDE indicates its effectiveness in mitigating the instructional challenges associated with content creation. Educators reported a significant reduction in the time and effort required to develop course materials, without compromising on the breadth or depth of the content. Moreover, the use of GenAI has shown promise in deterring conventional cheating methods, suggesting a positive impact on academic integrity and student engagement.

Contribution: This Full paper in the Research Category track describes a practical, scalable platform that seamlessly integrates Generative AI (GenAI) with online educational forums, offering a novel approach to augment the instructional capabilities of staff. The platform empowers instructional staff to efficiently manage, refine, and approve responses by facilitating interaction between student posts and a Large Language Model (LLM). Background: This study is anchored in Vygotsky's socio- cultural theory, with a particular focus on the concept of the More Knowledgeable Other (MKO). It examines how GenAI can augment the instructional capabilities of course staff in educational environments, acting as an auxiliary MKO to facilitate an enriched educational dialogue between students and instructors. This theoretical backdrop is important for understanding the integration of AI within educational contexts, suggesting a balanced collaboration between human expertise and artificial intelligence to enhance the learning and teaching experience. Research Question: How effective is GenAI in reducing the workload of instructional staff when used to pre-answer student questions posted on educational discussion forums? Methodology: Employing a mixed-methods approach, our study concentrated on select first and second-year computer programming courses with significant enrollments. The investigation involved the use of an AI -assisted platform by designated (human) Teaching Assistants (AI- TAs) to pre-answer student queries on educational forums. Our analysis includes a qualitative examination of feedback and interactions, focusing on the AI-TAs' experiences and perceptions. While we primarily analyzed efficiency indicators such as the frequency of modifications required to AI generated responses, we also explored broader qualitative aspects to understand the impact and reception of AI -generated responses within the educational context. This approach allowed us to gather insights into both the quantitative engagement with AI -assisted posts and the qualitative sentiments expressed by the instructional staff, laying the groundwork for further in-depth analysis. Findings: The findings indicate no significant difference in student reception to responses generated by AI - TAs compared to those provided by human instructors. This suggests that GenAl can effectively meet educational needs when adequately managed. Moreover, AI - TAs experienced a reduction in the cognitive load required for responding to queries, pointing to GenAI's potential to enhance instructional efficiency without compromising the quality of education.