An appraisal of AI-powered automatic summarization tools in Enugu State College of Education Library, Enugu State
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- 1-5 Chapters
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- NGN 5000
Background of the study
Automatic text summarization, driven by advances in natural language processing and deep learning, offers libraries the ability to condense lengthy documents into concise, coherent abstracts, thereby enhancing user efficiency in literature review and information retrieval (Nguyen, 2024). In academic settings, summarization tools—both extractive and abstractive—have reduced reading time by up to 60% while maintaining high fidelity to source content (Smith, 2023). Enugu State College of Education Library has piloted AI summarizers on curriculum guides, faculty publications, and student theses to aid educators and learners in quickly grasping key findings (Eze, 2025). However, challenges persist: abstractive models sometimes generate factual inaccuracies, while extractive approaches may miss nuanced insights (Perez, 2024). Furthermore, local linguistic variations and domain‑specific terminology in education research can degrade summarization quality (Okoro, 2025). Evaluating these tools’ performance within the College’s digital ecosystem is essential to determine their suitability, inform training needs, and guide integration strategies that maximize pedagogical impact and user adoption.
Statement of the problem
Despite deploying AI summarizers, faculty and students at Enugu State College of Education report inconsistent summary accuracy and occasional omission of critical pedagogical concepts, leading to mistrust of automated outputs (Ibrahim, 2024). Without empirical appraisal of summarization quality, usability, and impact on research workflows, library management cannot make informed decisions about tool selection or develop effective user support strategies.
Objectives of the study
To evaluate the accuracy and coherence of AI-generated summaries for educational research documents.
To assess the impact of summarization tools on users’ literature review efficiency.
To identify factors influencing user trust and adoption of AI summarizers.
Research questions
How do AI-generated summaries compare to human-written abstracts in accuracy and coverage?
To what extent do summarization tools reduce time spent on document review?
What user and system characteristics affect trust in AI-generated summaries?
Significance of the study
This appraisal will provide actionable insights for library and academic leaders on the effectiveness of AI summarization in supporting teaching and research. By identifying tool strengths and limitations, the study informs training programs, integration workflows, and investment decisions that enhance information literacy and research productivity in the College community.
Scope and limitations of the study
This study focuses on AI summarization of English‑language academic documents within the Enugu State College of Education Library. It excludes non‑textual materials (e.g., videos) and summarizers outside the piloted platforms. Limitations include variability in document complexity and the evolving nature of AI models.
Definitions of terms
Extractive summarization: AI method selecting key sentences verbatim from source texts.
Abstractive summarization: AI method generating novel sentences that capture source meaning.
Natural language processing (NLP): AI subfield enabling machines to interpret and generate human language.
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