Browsing by Author "Kituku, Benson"

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    Integrating Generative Artificial Intelligence in Assessment Generation for Higher Education: Computer Science Use Case
    (IST-Africa, 2025) Kituku, Benson; Araka, Eric; Muuro, Elizaphan
    The overburdened lecturers today face the dual challenge of monitoring online attendance and ensuring active student engagement, while also receiving instant feedback on concept comprehension during live lectures. They are further pressed to extend practical problem-solving experiences beyond the classroom and deliver higher-order thinking assessments - all without increasing their workload. In response, this paper presents a one-year experiment involving 590 students across seven computer science units. The study integrated generative AI-generated questions into live lectures, weekly discussion forums, and both formative and summative exams. The findings reveal that, with ethical use and proper human-inthe-loop, AI can significantly boost student engagement, promptly rectify misconceptions, and foster collaborative learning outside traditional settings and offer diverse question styles. However, given potential pitfalls such as low-quality outputs and overreliance, instructors must adhere to best practices and maintain rigorous oversight to ensure that assessments remain balanced, engaging, and of high quality, eventually benefiting both educators and learners.
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    Students’ Perceptions on the Use of Generative AI in Enhancing Teaching and Learning Computer Science Courses
    (IST-Africa, 2025) Mwaniki, Susan; Araka, Eric; Kituku, Benson; Maina, Elizaphan
    This research study examines the perception of third-year computer science students in Kenya towards the use of Generative Artificial Intelligence tools in their studies. The researchers used descriptive research design to understand student attitudes, the perceived usefulness of Generative Artificial Intelligence, and the challenges they face. The study finds that students generally see Generative Artificial Intelligence tools as beneficial for learning, especially in areas like coding and research. However, they also identify concerns about over-reliance on Generative Artificial Intelligence, accuracy of information, and ethical considerations, such as plagiarism. The study concludes that Generative Artificial Intelligence can be valuable in computer science education, however, it should be used reliably and balanced with traditional teaching methods to ensure critical thinking and creativity.