Developing critical thinking through academic texts in a foreign language
Researchers from three Ukrainian universities examined how reading academic texts in a foreign language can help higher-education students develop critical thinking in conditions characterized by volatility, uncertainty, complexity, and ambiguity.
The authors combined narrative review, critical content analysis, and elements of grounded theory. Their goal was not simply to summarize prior work, but to build an exploratory conceptual model connecting foreign-language academic reading with motivation, reasoning, writing, and the wider demands of contemporary higher education. The article was published in Sapienza: International Journal of Interdisciplinary Studies.
Where Evidano fit
The team searched Wiley, ResearchGate, ScienceDirect, MDPI, and ERIC using terms related to critical thinking, higher education, development of critical thinking, and academic reading in a foreign language. That search produced an initial corpus of 126 items.
The corpus was analyzed with Evidano to surface categories and help the researchers identify patterns across the literature. The article names VUCA education, Generation Z student motivation, and adaptive reasoning among the categories produced at this stage.
Those categories then informed a final search and selection of material for manual narrative review and content analysis. In other words, Evidano helped orient the synthesis; the authors retained responsibility for source selection, interpretation, and construction of the final model.
What the study concluded
The article positions academic reading in a foreign language as more than vocabulary or comprehension practice. Properly designed tasks can ask students to compare arguments, question assumptions, integrate evidence, recognize ambiguity, and move between disciplinary and cultural viewpoints.
The review links these activities with adaptive reasoning and with the demands placed on Generation Z students in uncertain, technology-rich environments. It also emphasizes that motivation matters: students need to understand why difficult academic reading is relevant and need structured support to persist through it.
The resulting model offers a research agenda rather than a claim of causal proof. It organizes relationships that future studies can test in classrooms and across disciplines.
Why this case matters
This case extends Evidano’s use beyond interviews and survey comments into literature synthesis. It demonstrates a bounded role for AI in a review: create an initial map of a large, interdisciplinary corpus, then let researchers decide which categories are meaningful, which sources belong in the final analysis, and how the evidence fits together.
For teams conducting narrative, scoping, or evidence reviews, the workflow illustrates how AI can reduce the early organizational burden without obscuring the need for explicit search terms, manual selection, source appraisal, and theory-building.
Limits and responsible interpretation
The review is narrative and conceptual, not a systematic review or meta-analysis. The paper does not report a PRISMA flow, duplicate screening, or a formal quality appraisal of all 126 items, so the Evidano-assisted categories are part of an exploratory workflow rather than a reproducible estimate of the literature.
The paper also does not benchmark Evidano against another review method. The strongest defensible claim is that the platform supported corpus organization and category development inside a human-led review.
How Evidano fit the workflow
- 1Search broadly
Five research platforms were queried with a defined set of critical-thinking and academic-reading terms.
- 2Assemble the initial corpus
The search yielded 126 sources spanning education, psychology, language learning, and related fields.
- 3Run an AI-assisted categorization
Evidano helped identify recurring concepts and possible organizing categories.
- 4Use categories to refine the search
The team used those concepts to focus the final evidence selection.
- 5Review and interpret manually
The retained material underwent narrative review and critical content analysis.
- 6Build the conceptual model
The authors used “critical pendulum” reasoning to develop testable propositions and a model for future research.
Evidano organized a literature corpus, not just transcripts or survey comments.
AI categories guided the search; selection and synthesis stayed manual.
Databases and search terms are reported in the paper.
The conceptual model and propositions are the authors’ own.