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Found 4 papers, 2 papers with code
Bigger Data or Fairer Data? Augmenting BERT via Active Sampling for Educational Text Classification
Pretrained Language Models (PLMs), though popular, have been diagnosed to encode bias against protected groups in the representations they learn, which may harm the prediction fairness of downstream models.
Towards Detecting AI-Generated Text within Human-AI Collaborative Hybrid Texts
Our empirical findings highlight (1) detecting AI-generated sentences in hybrid texts is overall a challenging task because (1. 1) human writers' selecting and even editing AI-generated sentences based on personal preferences adds difficulty in identifying the authorship of segments; (1. 2) the frequent change of authorship between neighboring sentences within the hybrid text creates difficulties for segment detectors in identifying authorship-consistent segments; (1. 3) the short length of text segments within hybrid texts provides limited stylistic cues for reliable authorship determination; (2) before embarking on the detection process, it is beneficial to assess the average length of segments within the hybrid text.
Towards Automatic Boundary Detection for Human-AI Collaborative Hybrid Essay in Education
Then we proposed a two-step approach where we (1) separated AI-generated content from human-written content during the encoder training process; and (2) calculated the distances between every two adjacent prototypes and assumed that the boundaries exist between the two adjacent prototypes that have the furthest distance from each other.
Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review
Educational technology innovations leveraging large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content.
