Ethiopian Students Tamed ChatGPT’s Blind Spots to Beat Writer’s Block

When a second-year student at Bahir Dar University in northwestern Ethiopia asked ChatGPT to help him write about buna, the traditional Ethiopian coffee ceremony, the chatbot confidently produced ideas about American coffee shops and Starbucks. The moment was small, but it was revelatory. The tool he had trusted to understand everything about writing in English, he realized, did not really understand Ethiopian tradition or how Ethiopians talk about it in Amharic. That single mismatched response set him, and eight of his classmates, on a semester-long journey from uncritical dependence on generative artificial intelligence toward something far more interesting: a deliberate, culturally grounded, and emotionally aware form of AI literacy that researchers have rarely documented in low-resource settings.

The episode comes from a new study published in Discover Education by Eshetie Kasie, an Ethiopian TEFL practitioner and researcher at Bahir Dar University. The research followed ten undergraduate English as a foreign language (EFL) students across one full academic semester as they used generative AI, mostly through mobile phones and often through patchy internet connections, to manage writer’s block. The findings extend an influential framework for critical AI literacy with two new layers, one emotional and one structural, and they offer a rare Global South perspective on a conversation that has been dominated by classrooms in wealthy, well-connected countries.

Writer’s block is not a trivial complaint. Researchers describe it as a tangle of cognitive overload, emotional strain, linguistic difficulty, and conflicts of identity that surface during composition, and these factors intensify in EFL contexts where students face limited proficiency, cultural expectations, and high-stakes assessment. A student who struggles to retrieve vocabulary in a second language often experiences that struggle as anxiety rather than as a purely linguistic gap, which then compounds the original difficulty. In Ethiopia, where English is the medium of instruction but Amharic and other local languages dominate everyday life, the blank page can feel especially unforgiving. Generative AI promised relief, but the study shows that the relief was conditional, arriving only when students learned to interrogate the tool rather than simply obey it.

The study’s theoretical anchor is the Affective Process and Strategic Engagement (APSE) framework, developed by researchers from first-year second-language writing classrooms in the United States. APSE identifies four interrelated dimensions of critical AI literacy: awareness of what AI can and cannot do, positionality toward its use, strategies for interacting with it, and evaluation of its output. But the framework was built in a setting that assumes stable internet access and Anglocentric rhetorical norms, assumptions that collapse quickly in a university where power outages are routine and connectivity is mobile-only. Kasie’s extension, which he calls APSE-Extended for Block, keeps the four core dimensions and wraps them in an inner affective layer addressing anxiety reduction, confidence building, and identity resilience, and an outer equity layer addressing connectivity constraints, linguistic hierarchies, and resistance to Anglocentric assumptions embedded in AI outputs.

The methodology was deliberately multimodal. Kasie purposefully selected ten undergraduates, five women and five men spanning first to third year, all of whom screened as experiencing moderate or high writer’s block severity. Over fourteen to sixteen weeks, he gathered semi-structured interviews before, during, and after the semester, weekly reflective journals, anonymized ChatGPT conversation logs, successive writing drafts, and classroom observations. Amharic portions of journals and interviews were professionally translated and checked against the originals. To quantify how critically students engaged with the technology, Kasie compared every AI-generated output in the conversation logs against the corresponding segment of each student’s revised draft, coding outputs as accepted, substantially revised, or rejected, and then corroborating those judgments in stimulated recall sessions where students explained their decisions.

The developmental pattern that emerged was strikingly consistent. Most participants began the semester trusting ChatGPT almost unconditionally for English writing tasks, then encountered inaccuracy, cultural misrepresentation, and Anglocentric bias the moment they applied it to Ethiopian content. One student asked for help drafting a reflection on Timkat, the Ethiopian Orthodox celebration of Epiphany, and received a generic description of a religious festival that omitted the procession of the tabot and the ritual blessing of water entirely. The student responded by researching and rewriting the section from memory and family accounts. By semester’s end, eight of the ten participants treated AI outputs as provisional starting points requiring cultural filtering rather than finished material.

Positionality, the framework’s term for how writers situate themselves relative to the tool, proved equally consequential. Eight participants actively reclaimed authorship and resisted perceived Western or native-speaker bias in AI outputs. A third-year student named Selam, given a pseudonym in the study, described how the AI consistently framed gender issues through a Western feminist lens, presenting women’s oppression as uniform across cultures. That framing, she said, did not reflect her own story, and she had to decide for herself what was true for Ethiopian women. Students revised or rejected content that flattened communal and relational framings common in Amharic argumentative writing into individualistic Western argument structures. A second-year student, Kidist, rejected outright an AI characterization of Ethiopian schools as underdeveloped and backward, rewriting the passage to say instead that her schools faced limited resources but also showed innovation and resilience.

The quantitative picture reinforces the qualitative one. Based on the draft-log comparisons, nine participants rejected an estimated 60 to 80 percent of AI-generated content overall, particularly when it appeared biased, generic, or culturally inappropriate, and they did not simply delete unwanted material but rewrote it to reflect their own framing and tone. Nine students also developed structured prompting strategies that functioned as affective scaffolding for block relief. A first-year student, Dawit, described asking ChatGPT for three short ideas about Ethiopian youth unemployment without full sentences, then using those fragments to begin his own writing. Others requested a single counterargument to test against their thesis, or an outline skeleton they filled in entirely with their own wording. All ten participants reported that careful, limited use of generative AI reduced anxiety and increased confidence, with one student describing AI as a helper, not a boss.

Yet the equity layer of the extended framework tells a sobering story. All ten participants faced persistent connectivity challenges and linguistic mismatch. Dawit explained that when the internet cuts off, his ideas disappear and he feels blocked again, a description of how an infrastructure gap translates directly into a personal interruption of the writing process. Students often drafted offline and revised only once connectivity returned. The study connects this lived constraint to broader scholarship: a systematic review of AI use in Global South EFL classrooms found that AI holds real potential for student autonomy only when implementation is context-sensitive and equity-driven, while research on South African higher education cautions against treating AI literacy as a purely technical skill separable from local conditions. Notably, one first-year student with high block severity copied full paragraphs from AI outputs without revision all semester, suggesting that block severity, not just year of study, shapes how readily students resist algorithmic norms.

The practical payoff is a four-phase classroom cycle that educators in similar contexts can adapt: an awareness phase in which students experiment with AI on local topics and discuss cultural mismatches as they arise; a positionality phase in which they revise AI content to incorporate Amharic rhetorical patterns and local perspectives; a strategies phase introducing spark prompting for ideas only, preserving ownership of the text; and an evaluation phase in which students apply a personal checklist judging whether an output respects Ethiopian realities and sounds like their own voice. Kasie is careful about the limits of his findings, which remain bound to ten students at a single university over one semester, and he calls for longitudinal and comparative research across urban and rural institutions. But the core insight travels well beyond Bahir Dar. Critical AI literacy, the study suggests, is not just a technical skill of writing better prompts. It is an emotional and political practice in which learners decide what the machine may say about them, and in which a coffee ceremony, a religious festival, or a school described as backward can become the moment a student stops outsourcing judgment and starts writing as themselves.

Subject of Research: Critical AI literacy and writer's block among Ethiopian EFL undergraduates using generative AI in low-resource settings

Article Title: Extending the affective process and strategic engagement framework to address writer’s block in Ethiopian English as a foreign language contexts through critical artificial intelligence literacy and ethical generative artificial intelligence in low-resource settings

Article References: Kasie, E. (2026). Extending the affective process and strategic engagement framework to address writer’s block in Ethiopian English as a foreign language contexts through critical artificial intelligence literacy and ethical generative artificial intelligence in low-resource settings. Discover Education, 5(1), Article 1133. https://doi.org/10.1007/s44217-026-02049-w

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02049-w

Keywords: critical AI literacy, generative AI, writer's block, EFL writing, Ethiopia, Bahir Dar University, APSE framework, Global South, equity, ChatGPT, second language writing, digital divide