The Hong Kong Association for Self-Access Learning and Development (HASALD) would like to invite you to attend the following seminar:
Multiparty Co-Learning and Critical AI Literacy: The developmental process of the automated speaking assessment for HKDSE English speaking paper
by Dr Nick Wong
Although AI-enabled edtech tools are proliferating in recent years for supporting various self-directed or blended learning activities, published accounts rarely explain how practitioners, technologists, and researchers co-produce these tools. This presentation documents a journey of multiparty co-learning (Li Wei, 2025) by a group of experienced language teachers, educational technologists, and applied linguists in a project that aims to develop an innovative self-directed edtech solution with AI-enabled assessment for the English speaking assessment in the Hong Kong Diploma of Secondary Education (HKDSE), and draws conceptual, methodological and practical lessons from the co-learning process that we believe would be of benefit to other researchers and practitioners who are interested in researching, developing, or utilising AI-enabled tools in language teaching, learning, and assessment.
Using a narrative, action-research approach, this study analysed project records, meeting notes, and 61 human-graded speaking exam recordings to map three iterative development cycles:
- Cycle 1: A deep learning-based acoustic-phonetic assessment model handled context-insensitive criteria—pronunciation, fluency, vocabulary, and grammar—achieving reliable bottom-up scores.
- Cycle 2: A large language model (LLM) was used to rate context-sensitive criteria (interaction; ideas & organisation). Despite enhanced rubrics drawn from practitioner expertise, its scoring diverged from human ratings.
- Cycle 3: Reflecting on these limits, the team designed a sequential pipeline: the phonetic model produced initial scores that conditioned the LLM, aligning machine output with human judgements (achieving low mean-absolute errors and high accuracy on all marking categories).
Findings show that multiparty co-learning—marked by role shifting, perspective understanding, and feedback sharing—was essential for resolving technical–pedagogical tensions and cultivating critical AI literacy, by interrogating AI’s affordances, biases, and classroom implications. These outcomes provide valuable guidance for developing AI tools in language education, highlighting co-learning and critical AI literacy as essential for ethical, innovative practices, and transferrable to other high-stake speaking exams.
Bio
Nick Wong is a Lecturer at the Center for Language Education, HKUST. He is an experienced practitioner in English for Specific Purposes (ESP) and an applied linguist specializing in translanguaging and multilingualism. Currently, he is the principal investigator of an EdB eLAFP project, developing an AI-assisted virtual reality English-speaking program for secondary students (HK$30,365,700.00). He has received multiple teaching awards, including the SHSS Teaching Award and the CLE Teaching Award at HKUST. He is also a Senior Fellow of the Higher Education Academy (SFHEA) and a Fellow of the Lancaster University China Centre.
- Date & Time: 20 October 2025, 6:30 to 7:30 PM
- Venue: HHB926,PolyU Hung Hom Bay Campus (Department of English and Communication)
Please register here by 18 October if you wish to attend this talk.
Thank you, and we hope to see you at our next talk!