# AI and Machine Learning for Next Generation Science Assessments

arXiv: https://arxiv.org/abs/2405.06660 · HTML: https://mlsanity.com/paper/2405.06660/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 40 references that are in the map.
- First posted on arXiv 23 Apr 2024.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Fine-tuning ChatGPT for Automatic Scoring](https://mlsanity.com/paper/2310.10072/) (2023)
- [Context Matters: A Strategy to Pre-train Language Model for Science Education](https://mlsanity.com/paper/2301.12031/) (2023)
- [Using GPT-4 to Augment Unbalanced Data for Automatic Scoring](https://mlsanity.com/paper/2310.18365/) (2023)
- [Matching Exemplar as Next Sentence Prediction (MeNSP): Zero-shot Prompt Learning for Automatic Scoring in Science Education](https://mlsanity.com/paper/2301.08771/) (2023)
- [Pseudo AI Bias](https://mlsanity.com/paper/2210.08141/) (2022)

## Built on by (most cited first, 3 of 3)

- [Fine-tuning ChatGPT for Automatic Scoring of Written Scientific Explanations in Chinese](https://mlsanity.com/paper/2501.06704/) (2025)
- [Artificial Intelligence Bias on English Language Learners in Automatic Scoring](https://mlsanity.com/paper/2505.10643/) (2025)
- [Developing a Multi-Agent System to Generate Next Generation Science Assessments with Evidence-Centered Design](https://mlsanity.com/paper/2602.18451/) (2026)

## Abstract (arXiv)

This chapter focuses on the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in science assessments. The paper begins with a discussion of the Framework for K-12 Science Education, which calls for a shift from conceptual learning to knowledge-in-use. This shift necessitates the development of new types of assessments that align with the Framework's three dimensions: science and engineering practices, disciplinary core ideas, and crosscutting concepts. The paper further highlights the limitations of traditional assessment methods like multiple-choice questions, which often fail to capture the complexities of scientific thinking and three-dimensional learning in science. It emphasizes the need for performance-based assessments that require students to engage in scientific practices like modeling, explanation, and argumentation. The paper achieves three major goals: reviewing the current state of ML-based assessments in science education, introducing a framework for scoring accuracy in ML-based automatic assessments, and discussing future directions and challenges. It delves into the evolution of ML-based automatic scoring systems, discussing various types of ML, like supervised, unsupervised, and semi-supervised learning. These systems can provide timely and objective feedback, thus alleviating the burden on teachers. The paper concludes by exploring pre-trained models like BERT and finetuned ChatGPT, which have shown promise in assessing students' written responses effectively.