UNPACKING ACADEMIC CONFUSION: SOCIAL MEDIA, SOCIAL NETWORKING, SEARCH ENGINES, AND AI AT ULPGL - A DIGITAL LITERACY THEORY APPROACH

This intrinsic qualitative case study examines academic confusion as an experiential phenomenon emerging where social media, social networking, search engines, and artificial intelligence (AI) intersect in university learning environments, using Digital Literacy Theory as a sensitizing framework. Drawing on a purposive sample of students and faculty at the Universite Libre des Pays des Grands Lacs(ULPGL)and situating findings within current empirical, bibliometric, and multivocal literatures, the study identifies four thematic portraits of confusion epistemological muddle, misapplied tools, opaque algorithmic authority, and fractured academic identity-and interprets these through a seven domain digital literacy architecture.Data were generated from a purposive sample at the Universite Libre des Pays des Grands Lacs (ULPGL) comprising 30 undergraduate and postgraduate students who actively use AI tools (e.g., ChatGPT), Google Scholar like scholarly discovery, and social media for learning; 10lecturers/faculty negotiating the integration or resistance of digital tools in pedagogy; 6librarians/IT staff who mediate information access and tool support; and 4 administrative staff involved in (or affected by) AI and/or LMS deployment decisions. The study design foregrounds the institutional ecosystem around GenAI, consistent with prior research showing that student practice and faculty response co-produce both opportunities(e.g., feedback and learning support)and vulnerabilities (e.g., opaque tool limits and integrity risks) (Ortiz-Bonnn& Blahopoulou, 2025; ,Firat, 2023).The paper offers a context sensitive implementation roadmap and evaluation strategy for ULPGL and comparable Global South institutions, emphasizing embedded curriculum, algorithmic/AI literacy, infrastructural investments, assessment redesign, and socio emotional supports.