Can LLMs Grade Short-answer Reading Comprehension Questions :
  Foundational Literacy Assessment in LMICs
Owen Henkel Libby Hills Bill Roberts Joshua McGrane
This paper presents emerging evidence of using generative large language
models (i.e., GPT-4) to reliably evaluate short-answer reading comprehension
questions. Specifically, we explore how various configurations of generative
(LLMs) are able to evaluate student responses from a new dataset, drawn from a
battery of reading assessments conducted with over 150 students in Ghana. As
this dataset is novel and hence not used in training runs of GPT, it offers an
opportunity to test for domain shift and evaluate the generalizability of
generative LLMs, which are predominantly designed and trained on data from
high-income North American countries. We found that GPT-4, with minimal prompt
engineering performed extremely well on evaluating the novel dataset (Quadratic
Weighted Kappa 0.923, F1 0.88), substantially outperforming transfer-learning
based approaches, and even exceeding expert human raters (Quadratic Weighted
Kappa 0.915, F1 0.87). To the best of our knowledge, our work is the first to
empirically evaluate the performance of generative LLMs on short-answer reading
comprehension questions, using real student data, and suggests that generative
LLMs have the potential to reliably evaluate foundational literacy. Currently
the assessment of formative literacy and numeracy is infrequent in many low and
middle-income countries (LMICs) due to the cost and operational complexities of
conducting them at scale. Automating the grading process for reading assessment
could enable wider usage, and in turn improve decision-making regarding
curricula, school management, and teaching practice at the classroom level.
Importantly, in contrast transfer learning based approaches, generative LLMs
generalize well and the technical barriers to their use are low, making them
more feasible to implement and scale in lower resource educational contexts.