Context: The emergence of Generative AI (GenAI) technology presents an opportunity to enhance students' learning experience in programming courses using pair programming. GenAI can take the navigator role in student-GenAI pairing as an alternative to traditional student-student pairing. Objective: This study explored various use cases, challenges, and learning experiences IT students faced when using the student-GenAI pairing approach. Method: We integrated GenAI into CS1 and CS2 courses in one semester split into two halves: in the first half, students worked in student-GenAI pairs, while in the second half students worked in traditional student-student pairing with GenAI as an additional reinforcement. At the end of the semester, we interviewed 12 students purposefully selected out of 103 enrollments and employed a thematic analysis approach to synthesize the qualitative data. Results: We identified five distinct GenAI use cases confirming the existing studies and matching how software practitioners utilize GenAI in the industry, indicating an alignment between education and industry practice. Furthermore, we identified six challenges. One novel challenge related to the consequence of the technology is narrowing the students' learning horizons. The students also expressed a lack of engagement and empathy in student-GenAI pairing. They preferred the traditional pairing with GenAI as additional support, providing a better learning experience. Conclusion: Integrating GenAI into programming courses can enhance the learning experience, but new challenges emerge, provoking further studies to address them.

Context: Generative AI (GenAI) brings new op-portunities to the software industry and the digital economy in a broader context. Objective: This study aimed to explore and capture the practitioners' perception of GenAI adoption in the fast-paced software industry in the context of developing countries. Method: We conducted online focus group discussions with 18 practitioners from various roles to collect qualitative data. The practitioners have an average of 7.8 years of working experience and have used GenAI for over a year. We employed thematic analysis and the Human-AI Collaboration and Adaptation Framework (HACAF) to identify the influencing factors of GenAI adoption, such as awareness, use cases, and challenges. Results: The adoption of GenAI technology is evident from practitioners. We identified 22 practical use cases, three of which were novel, i.e., contextualizing solutions, assisting the internal audit process, and benchmarking the internal software development process. We also discovered seven key challenges associated with the GenAI adoption, two of which were novel, namely, no matching use cases and unforeseen benefits. These challenges slow GenAI adoption and potentially hinder developing countries from entering a high-skill industry. Conclusion: While the adoption of GenAI technology is promising, industry-academia collaboration is needed to find solutions and strategies to address the challenges and maximize its potential benefits.