Dr. Christy Macnish FHEA

Aim
This work‑in‑progress examines how institutional generative artificial intelligence (GenAI) policy and principles can be translated into inclusive academic skills pedagogy that supports critical thinking and the academic success of culturally and linguistically diverse (CALD) students.

Background/context
GenAI is embedded in contemporary higher education. TEQSA guidance emphasises that detection‑led approaches are not viable and that institutions must support ethical and responsible GenAI use (Lodge et al., 2025). However, institutional approaches to implementing GenAI policy vary, creating gaps between policy intent and teaching practice (An et al., 2025). This matters for equity: where CALD English speakers often have ideas undervalued due to non-standard academic English use (Weng & Fu, 2025), GenAI tools can promote equity by offering personalised feedback (Guan et al., 2025).

Description
A new 2025 pre-semester academic skills workshop positioned GenAI as one of several feedback tools (alongside learning advisers and Studiosity). Guided by institutional GenAI principles, the workshop emphasised contextual prompting, critical evaluation of GenAI outputs, student decision‑making authority, routine acknowledgment of use, and preservation of student voice. This aligns with research showing GenAI feedback supports writing quality when combined with critical revision (Mekheimer, 2025; Zhang et al., 2025).

Method
Evaluation draws on post‑workshop student feedback collected via a Microsoft Form (n=22), focusing on confidence, feedback literacy, and help‑seeking behaviours, alongside practitioner reflection on workshop design.

Evidence
Preliminary findings indicate increased confidence and engagement with feedback processes, including improved error detection and greater willingness to seek academic writing support. Student feedback noted 68% improved error detection, 77% increased confidence, and 100% increased likelihood of seeking writing feedback.

Contribution
The poster seeks feedback on transferability of this policy‑to‑practice framework, approaches to evaluating equity‑focused GenAI initiatives within professional practice contexts, and strategies for scaling responsible GenAI use across academic skills support.

Statement of relevance to research and development in higher education
As higher education integrates generative artificial intelligence (GenAI) into teaching and learning, institutions face the challenge of translating policy and principles into inclusive and ethical educational practice. Recent guidance emphasises that institutions must support responsible GenAI use rather than rely on detection‑led approaches (Lodge et al., 2025), yet research highlights persistent gaps between policy intent and pedagogical implementation (An et al., 2025). This work addresses a key priority for research and development in higher education: how GenAI‑informed academic skills support can be designed to promote equity for culturally and linguistically diverse (CALD) students.

CALD students often experience disadvantage when academic success is closely linked to proficiency in standard academic English, resulting in ideas being undervalued. Emerging research suggests GenAI can support more equitable engagement through personalised feedback, provided student agency, transparency, and voice are maintained (Weng & Fu, 2025; Guan et al., 2025). This work contributes practice‑based insights into how institutional GenAI principles can be enacted through academic skills pedagogy led by professional staff. By foregrounding human‑centred values, fairness, and academic integrity, the project informs scalable approaches to responsible GenAI integration that support inclusive learning environments during a critical period of sector‑wide transformation.

References

An, Y., Yu, J. H., & James, S. (2025). Investigating the higher education institutions' guidelines and policies regarding the use of generative AI in teaching, learning, research, and administration. International Journal of Educational Technology in Higher Education, 22(1), Article 10. https://doi.org/10.1186/s41239-025-00507-3 

Guan, L., Zhang, E. Y., & Gu, M. M. (2025). Examining generative AI–mediated informal digital learning of English practices with social cognitive theory: A mixed-methods study. ReCALL (Cambridge, England), 37(3), 315–331. https://doi.org/10.1017/S0958344024000259

Lodge, J. M., Bearman, M., Dawson, P., Gniel, H., Harper, R., Liu, D., McLean, J., & Ucnik, L. (2025). Enacting assessment reform in a time of artificial intelligence. Tertiary Education Quality and Standards Agency, Australian Government. https://www.teqsa.gov.au/sites/default/files/2025-09/enacting-assessment-reform-in-a-time-of-artificial-intelligence.pdf

Mekheimer, M. (2025). Generative AI-assisted feedback and EFL writing: A study on proficiency, revision frequency and writing quality. Discover Education, 4(170), 1–20. https://doi.org/10.1007/s44217-025-00602-7

Weng, Z., & Fu, Y. (2025). Generative AI in language education: Bridging divide and fostering inclusivity. International Journal of Technology in Education, 8(2), 395–420. https://doi.org/10.46328/ijte.1056

Zhang, Z., Aubrey, S., Huang, X., & Chiu, T. K. F. (2025). The role of generative AI and hybrid feedback in improving L2 writing skills: A comparative study. Innovation in Language Learning and Teaching, 1–19. https://doi.org/10.1080/17501229.2025.2503890