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@article{191334,
        author = {Jheel D. Barad},
        title = {A Contextualized Framework for AI Pedagogical Competence in Higher Education English Literature},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {12},
        number = {no},
        pages = {1287-1294},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191334},
        abstract = {The disruptive capabilities of Generative Artificial Intelligence (AI) pose a significant challenge to established pedagogical practices in Higher Education (HE), particularly within English Literature classrooms. In this evolving context, traditional passive teaching models long critiqued by Elaine Showalter are increasingly rendered inadequate. Although comprehensive frameworks such as the UNESCO AI Competency Framework and the European DigCompEdu provide valuable guidance, their generic nature limits their applicability to discipline-specific pedagogical concerns. Notably, they fail to address the distinctive demands of literary interpretation, authorship authenticity, and critical textual analysis.
Drawing on an extensive theoretical synthesis, this paper critiques the limitations of these broad frameworks by employing TPACK (Technological Pedagogical Content Knowledge) as a conceptual anchor. It argues that AI’s instructional potential, interpreted through Bloom’s 2 Sigma Problem, legitimizes the delegation of lower-order literary tasks such as summarization and contextual retrieval to AI tools. This redistribution of cognitive labour enables classroom time to be reoriented toward higher-order literary engagement. Such a shift necessitates an Inverted Bloom’s Taxonomy, wherein students initially engage in AI-assisted ‘Creation’ outside the classroom and subsequently focus on in-class ‘Analysis’ and ‘Evaluation’.
Based on this synthesis, the paper proposes the AI-Pedagogy for Literary Interpretation and Criticality Framework, which introduces a crucial discipline-specific dimension: Hermeneutics and Bias. This dimension equips faculty to train students to interrogate AI-generated texts as emergent cultural artifacts shaped by ideological, historical, and algorithmic biases. The framework offers a theoretical blueprint for English Literature departments seeking to redesign curricula and cultivate targeted AI pedagogical competencies, ensuring the preservation and deepening of critical thinking in the AI-mediated educational landscape.},
        keywords = {Artificial Intelligence, Pedagogical Competence, English Literature, DigCompEdu, TPACK, Critical Hermeneutics},
        month = {},
        }