We analyzed data from 25,969 test takers of a high-stakes, computer-adaptive English proficiency test to examine relationships between repeated use of AI-generated practice tests and performance, affect, and score-sharing behavior. Taking 1–3 practice tests was associated with higher scores and confidence, while higher usage showed different engagement and outcome

The popularization of large language models (LLMs) such as OpenAI’s GPT-3 and GPT-4 have led to numerous innovations in the field of AI in education. With respect to automated writing evaluation (AWE), LLMs have reduced challenges associated with assessing writing quality characteristics that are difficult to identify automatically, such as discourse coherence. In addition, LLMs can provide rationales for their evaluations (ratings) which increases score interpretability and transparency. This paper investigates one approach to producing ratings by training GPT-4 to assess discourse coherence in a manner consistent with expert human raters. The findings of the study suggest that GPT-4 has strong potential to produce discourse coherence ratings that are comparable to human ratings, accompanied by clear rationales. Furthermore, the GPT-4 ratings outperform traditional NLP coherence metrics with respect to agreement with human ratings. These results have implications for advancing AWE technology for learning and assessment.

Essay scoring is a critical task used to evaluate second-language (L2) writing proficiency on high-stakes language assessments. While automated scoring approaches are mature and have been around for decades, human scoring is still considered the gold standard, despite its high costs and well-known issues such as human rater fatigue and bias. The recent introduction of large language models (LLMs) brings new opportunities for automated scoring. In this paper, we evaluate how well GPT-3.5 and GPT-4 can rate short essay responses written by L2 English learners on a high-stakes language assessment, computing inter-rater agreement with human ratings. Results show that when calibration examples are provided, GPT-4 can perform almost as well as modern Automatic Writing Evaluation (AWE) methods, but agreement with human ratings can vary depending on the test-taker’s first language (L1).

Writing Mentor is a free Google Docs add-on designed to provide feedback to struggling writers and help them improve their writing in a self-paced and self-regulated fashion. Writing Mentor uses natural language processing (NLP) methods and resources to generate feedback in terms of features that research into post-secondary struggling writers has classified as developmental (Burstein et al., 2016b). These features span many writing sub-constructs (use of sources, claims, and evidence; topic development; coherence; and knowledge of English conventions). Prelimi- nary analysis indicates that users have a largely positive impression of Writing Mentor in terms of usability and potential impact on their writing.

Automated scoring of written and spoken responses is an NLP application that can significantly impact lives especially when deployed as part of high-stakes tests such as the GRE® and the TOEFL®. Ethical considerations require that automated scoring algorithms treat all test-takers fairly. The educational measurement community has done significant research on fairness in assessments and automated scoring systems must incorporate their recommendations. The best way to do that is by making available automated, non-proprietary tools to NLP researchers that directly incorporate these recommendations and generate the analyses needed to help identify and resolve biases in their scoring systems. In this paper, we attempt to provide such a solution.

Writing is a challenge, especially for at-risk students who may lack the prerequisite writing skills required to persist in U.S. 4-year postsecondary (college) institutions. Educators teaching postsecondary courses requiring writing could benefit from a better understanding of writing achievement and its role in postsecondary success. In this paper, novel exploratory work examined how automated writing evaluation (AWE) can inform our understanding of the relationship between postsecondary writing skill and broader success outcomes. An exploratory study was conducted using test-taker essays from a standardized writing assessment of postsecondary student learning outcomes. Findings showed that for the essays, AWE features were found to be predictors of broader outcomes measures: college success and learning outcomes measures. Study findings illustrate AWE’s potential to support educational analytics – i.e., relationships between writing skill and broader outcomes – taking a step toward moving AWE beyond writing assessment and instructional use cases.

We demonstrate a method of improving a seed sentiment lexicon developed on essay data by using a pivot-based paraphrasing system for lexical expansion coupled with sentiment profile enrichment using crowdsourcing. Profile enrichment alone yields up to 15% improvement in the accuracy of the seed lexicon on 3-way sentence-level sentiment polarity classification of essay data. Using lexical expansion in addition to sentiment profiles provides a further 7% improvement in performance. Additional experiments show that the proposed method is also effective with other subjectivity lexicons and in a different domain of application (product reviews).