Abstract Socializing students to Academic Integrity (AI) in the face of great cultural, linguistic and socioeconomic diversity in the student population in higher education calls for innovative strategies that are aligned with equity, diversity and inclusion principles. Through a mixed method of quantitative analysis of learner engagement data from the Learning Management System (LMS) and analysis of anonymous evaluation survey, along with thematic analysis of students’ open-ended responses in the evaluation survey, the authors explored how students responded to AI Socialization during a 4-week non-credit, online co-curricular program called ‘Reading and Writing Excellence’ (RWE). Nine groups of undergraduate students ( N =182) from 34 disciplines in different global locations during the COVID-19 pandemic were introduced to a curated set of AI online resources. Through a learner-driven, instructor-facilitated approach the AI Socialization also engaged students in language development and empowered them to communicate about their disciplinary course topics through written journal entries, receiving instructor feedback that increased their cultural and linguistic capital for further academic writing. This approach led to a high volume of written output (on average 6064 words per student written over a 4-week period). Nonparametric ANOVA was used to establish that low-proficiency students were able to produce as much written output as their more proficient peers. Survey results for various aspects important to academic integrity show students’ self-perception of readiness for academic writing: paraphrasing and summarizing (92%); organization of ideas (92%); critical thinking (93%); logic/argument (92%). Insights gained about educative engagement, language development and learner empowerment that can help students from diverse backgrounds to avoid Academic Integrity Violations (AIVs) and gain transformative access and success in higher education are incorporated into a set of recommendations that are applicable to a wide range of teaching contexts.
Stored only when both papers are in the corpus — 30 citations exist upstream.
Imported from OpenAlex (CC0) — nothing here is generated.