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Implementing Quilgo to Monitor Online Learning Evaluations: A Qualitative Case Study Nursyifa Azzahro; Citra Dewi; Naelul Rohmah
Journal of General Education and Humanities Vol. 4 No. 3 (2025): August
Publisher : MASI Mandiri Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58421/gehu.v4i3.628

Abstract

The need for a secure and efficient educational evaluation system in the digital era has been growing, especially with the increasing adoption of online exams. However, security and integrity issues remain significant challenges for educational institutions. This study aims to evaluate the effectiveness of Quilgo in addressing these challenges through its personalized registration system and proctoring features. This research involved observations of several educational institutions implementing online exams using Quilgo. It also included interviews with educators and exam participants as the main data sources. The participants were teachers and students with direct experience using Quilgo in various exam settings. The study begins with data collection through direct observation of Quilgo's use in several educational institutions that have adopted online exams and interviews with educators and exam participants. The collected data include user experiences related to registration ease, technical obstacles, and the effectiveness of proctoring features in preventing cheating. The analysis used qualitative and quantitative approaches to assess the application’s efficiency and reliability across different exam scenarios. Moreover, the data analysis process was carried out using behaviorism theory (Skinner, 1963), which highlights the presence of stimuli, responses, and reinforcement—both positive and negative—in the learning evaluation process. The research results show that using personalized link-based registration in Quilgo facilitates registration and enhances security, especially when combined with domain-based email restrictions. Moreover, the proctoring feature that monitors the visual activities of exam participants proved effective in maintaining exam integrity, although challenges related to internet connectivity and device compatibility remain. Despite remaining challenges such as internet connectivity and device compatibility, the results imply that Quilgo has the potential to support more trustworthy and effective online examinations in educational settings.
Who Am I as a Learner in the Age of Artificial Intelligence?: Exploring University Students' Identity Construction in AI-Mediated Learning Roy Venketsamy; Naelul Rohmah
DUTIES: Education and Humanities International Journal Vol. 2 No. 2 (2026): DUTIES: Education and Humanities International Journal
Publisher : CV. Akademi Merdeka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70152/duties.v2i2.362

Abstract

The rapid integration of artificial intelligence (AI) into higher education has transformed how students access information, engage with learning tasks, and construct knowledge. While previous studies have primarily focused on the adoption, effectiveness, and ethical implications of AI-assisted learning, limited attention has been given to how AI influences students’ identities as learners. This study explores how university students construct their learner identities in the era of artificial intelligence. Employing a qualitative phenomenological design, the study involved six higher education students who regularly used generative AI technologies to support their academic learning. Data were collected through semi-structured interviews and a focus group discussion and analyzed using inductive thematic analysis. The findings revealed four interconnected themes: becoming an AI-enhanced learner, negotiating independence and dependence in learning, redefining competence and expertise, and envisioning future-ready learner identities. Participants perceived AI as a learning companion that enhanced their capabilities while simultaneously requiring them to negotiate issues of autonomy, responsibility, and authenticity. The findings further indicated that students increasingly defined competence in terms of critical evaluation and effective human-AI collaboration rather than information acquisition alone. Moreover, participants’ engagement with AI was strongly connected to their aspirations for future academic and professional participation. The study contributes to the growing literature on AI in higher education by highlighting the identity-related dimensions of AI-mediated learning and offering insights into what it means to be a learner in an increasingly AI-driven educational landscape.