Book Review: AI in Language Teaching, Learning, and Assessment F Pan (ed.) AI in Language Teaching, Learning, and AssessmentIGI Global: Hershey, 2024; 384 pp.: ISBN 979-8-3693-0872-1.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The role of artificial intelligence (AI) in language and literacy education has gained accumulated attention (Davin, 2024).This book, edited by Fang Pan, a volume in the Advances in Educational Technologies and Instructional Design Book Series, provides a comprehensive exploration of AI's applications, benefits, challenges and future trajectories in language education through 15 chapters divided into 3 sections, along with a detailed compilation of references.At the macro level, Section 1 presents a dichotomy of views on the inevitable integration of AI into language education.In chapter 1, Liu systematically reviews 12 empirical studies, clarifying ChatGPT's dual nature of potential and risk.Chapter 2, by Singha et al., further emphasizes AI's role in customizing teaching materials and providing ongoing real-time evaluation through five language-learning platforms.Both chapters highlight the promising future of AI while raising concerns about ethics, privacy and academic integrity.Sharing similar apprehensions, contextualized within an undergraduate Italian course in Canada, Lobalsamo et al. note potential incoherence in composition and misrepresentation of the student's language level in chapter 3.Section 2 presents varied stakeholders' perspectives.Chapter 4, by Barrios-Beltran, uses a mixed-methods approach to analyze perceptions of AI usage in second language (L2) education among tertiary-level teachers and 38 learners in the US.While recognizing the significance of AI, learners do not view it as indispensable and raise ethical concerns limiting their AI use.Educators favor AI for academic purposes but demonstrate limited actual implementation.In chapter 5, Dincer and Bal conducted semi-structured interviews with 21 in-service English as a Foreign Language (EFL) educators, highlighting the value of AI adaptability to specific learning needs.This fits with Dincer's earlier qualitative work in which AI successfully conforms to standard aviation English patterns (Dincer et al., 2023).In chapter 6, through a mixed-methods approach, Uysal and Yüksel identify pre-service teachers' difficulties with AI-assisted lesson plans, particularly
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it