Evaluating AI-Enhanced Collaboration in EFL Academic Writing: A Longitudinal Mixed-Methods Study
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| Title: | Evaluating AI-Enhanced Collaboration in EFL Academic Writing: A Longitudinal Mixed-Methods Study |
|---|---|
| Language: | English |
| Authors: | Wei Guan (ORCID |
| Source: | Journal of Educational Computing Research. 2026 64(4):951-988. |
| Availability: | SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com |
| Peer Reviewed: | Y |
| Page Count: | 38 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Artificial Intelligence, Technology Uses in Education, English (Second Language), Second Language Learning, Academic Language, Writing Achievement, Student Attitudes, Blended Learning, Learner Engagement, Writing Skills, Technology Integration |
| DOI: | 10.1177/07356331251404736 |
| ISSN: | 0735-6331 1541-4140 |
| Abstract: | This longitudinal mixed-methods investigation addressed the unresolved question of how different modes of AI-enhanced collaboration shape EFL academic writing across content enrichment, linguistic accuracy, and organizational coherence. A four-arm, stratified, parallel-group randomized controlled trial (Randomized Controlled Trial [RCT]) with concealed, centralized allocation and assessor blinding was implemented over an 8-week hybrid program (2 h in-person +2 h online weekly; 32 contact hours). Randomization used permuted blocks (size = 8; 1:1:1:1) within university, baseline proficiency tertiles, and gender strata; analyses followed intention-to-treat with multiple imputation (m = 50; Rubin's rules) and prespecified sensitivity checks (complete-case, per-protocol). Quantitatively, outcomes were captured via rubric-based assessments, automated writing analytics, and AI engagement indices; qualitatively, semi-structured interviews (n = 88) and validated surveys were analyzed through reflexive thematic analysis. Integration occurred at design, methods, and interpretation using joint displays and triangulation. The content-enrichment intervention produced the largest and most durable gains (e.g., d > 0.80), including a substantial adjusted mean difference in overall writing versus control (Δ = 1.95, p < 0.001). Multivariate analyses (MANCOVA) showed a significant group effect after adjusting for pretest scores (p < 0.001). Path analysis indicated that perceived AI utility and digital literacy significantly mediated performance gains. Thematically, AI functioned as a collaborative partner, though dense feedback sometimes elevated cognitive load. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1502176 |
| Database: | ERIC |
| Abstract: | This longitudinal mixed-methods investigation addressed the unresolved question of how different modes of AI-enhanced collaboration shape EFL academic writing across content enrichment, linguistic accuracy, and organizational coherence. A four-arm, stratified, parallel-group randomized controlled trial (Randomized Controlled Trial [RCT]) with concealed, centralized allocation and assessor blinding was implemented over an 8-week hybrid program (2 h in-person +2 h online weekly; 32 contact hours). Randomization used permuted blocks (size = 8; 1:1:1:1) within university, baseline proficiency tertiles, and gender strata; analyses followed intention-to-treat with multiple imputation (m = 50; Rubin's rules) and prespecified sensitivity checks (complete-case, per-protocol). Quantitatively, outcomes were captured via rubric-based assessments, automated writing analytics, and AI engagement indices; qualitatively, semi-structured interviews (n = 88) and validated surveys were analyzed through reflexive thematic analysis. Integration occurred at design, methods, and interpretation using joint displays and triangulation. The content-enrichment intervention produced the largest and most durable gains (e.g., d > 0.80), including a substantial adjusted mean difference in overall writing versus control (Δ = 1.95, p < 0.001). Multivariate analyses (MANCOVA) showed a significant group effect after adjusting for pretest scores (p < 0.001). Path analysis indicated that perceived AI utility and digital literacy significantly mediated performance gains. Thematically, AI functioned as a collaborative partner, though dense feedback sometimes elevated cognitive load. |
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| ISSN: | 0735-6331 1541-4140 |
| DOI: | 10.1177/07356331251404736 |
