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The Automated Student Assessment Prize (ASAP) dataset, stands as one of the most commonly used publicly accessible resources Automated Essay Scoring (AES) tasks. This comprehensive dataset comprises a total of essays, encompassing responses to distinct prompts. Each essay has been evaluated and scored by human annotators. Essay sets are also accompanied by detailed scoring rubrics, each tailored with unique scoring guidelines and score ranges. These intricacies are essential as they cater to the multifaceted requirements and diverse scenarios of AES.
While the summary captures the essential elements, it glosses over or completely eliminates certain points and nuances found in the original. When complex or abstruse texts like The Federalist Papers are further scaled to, say, a seventh-grade reading level, they tend to become oversimplified and perhaps less useful than materials specifically written for younger audiences. As with most AI tools, every generated summary is slightly different, even when the same passage or text is used—so it can take a few tries to hit on a truly useful summary.
Similar to the model used in ’s work, we implemented a simple yet effective baseline model for score prediction based on BERT. This model integrated a fully connected prediction layer following the BERT output, and the BERT parameters remained unfrozen during training. Both the BERT model and the prediction layer were jointly trained on the training essay set (details in Appendix ).
Essentially asking other AP graders to grade the essay.
To learn how it works, we experimented with the tool as a teacher might. Pasting a link to , a 1,900-word Federalist Papers essay about checks and balances, and asking for a version at an 11th-grade reading level produces a crisp 500-word summary in less than 30 seconds. Though lacking in rhetorical flourish, the summary is more accessible to students looking to understand the main ideas. Where James Madison’s original text discusses the need for the independence of each branch of government, particularly in the “emoluments annexed to their offices,” Diffit offers a modern simplification: “The members of each branch of government should not rely too much on the other branches for their pay.”
Educator Michele Haiken as an ideal tool to try with an article that you “want to use for your lesson, but is too hard for some (or all) of your readers.” Recently, Kristen Starnes, who teaches high school social studies near Milwaukee, used the tool to adapt The Federalist Papers—a challenging collection of 18th-century essays—for her 11th-grade students. Though she might ask students to wrestle with the original text at first, she also provides a modern translation at a reading level appropriate for each student. “I have kids who have IEPs who are reading at different levels, and it’s really nice to have essentially the same reading, just leveled appropriately,” she says.
It might help to think of as an AI text leveler that can translate a given article, excerpt, or even YouTube video to any reading level from second to 11th grade. Using the same large language model technology that powers ChatGPT, Diffit parses complex texts (or captions from a video) and produces an accessible summary, along with a relevant vocabulary list with definitions; it can also generate assessment questions.
We spoke with educators and experimented with four of the most promising tools to learn how teachers can make AI work for them—without having to work too hard on AI. One thing to keep in mind: The technology behind these tools is still new, and all generated work should be examined for accuracy and adherence to standards. Also, it’s worth double-checking that tools comply with relevant local privacy regulations, .
1 use of AI grading, limited to your Common App essay
We introduce an extensive essay-scoring dataset, which includes 13,372 essays written by Chinese high school students. These essays are evaluated with multi-dimensional scores by expert educators. This dataset significantly enhances the resources available for AI in Education (AIEd).
1 use of AI grading, limited to your Common App essay
We pioneer the exploration of LLMs’ capabilities as AES systems, especially in complex scenarios featuring tailored grading criteria. Leveraging dual-process theory, our novel AES framework demonstrates remarkable accuracy, efficiency, and explainability.
AI grading unlocked for all essays
Automated Essay Scoring (AES) stands as a pivotal research area at the intersection of NLP and education. Traditional AES methods are usually regression-based or classification-based machine learning models trained with textual features extracted from the target essays. With the advancement of deep learning, AES has witnessed the integration of advanced techniques such as convolutional neural networks (CNNs) , long short-term memory networks (LSTMs) , and also pre-trained language models . These innovations have led to more precise score predictions, and state-of-the-art methods are primarily based on Bidirectional Encoder Representations from Transformers (BERT) .
AI grading unlocked for all essays
In this study, we explore the potential of proprietary and open-source LLMs such as GPT-3.5, GPT-4, and LLaMA3 for AES tasks. We conducted extensive experiments with public essay-scoring datasets as well as a private collection of student essays to assess the zero-shot and few-shot performance of these models. Additionally, we enhanced their effectiveness through supervised fine-tuning (SFT). Drawing inspiration from the dual-process Theory, we developed an AES system based on LLaMA3 that matches the grading accuracy and feedback quality of fine-tuned LLaMA3. Our human-LLM co-grading experiment further revealed that this system significantly improves the performance and efficiency of both novice and expert graders, offering valuable insights into the educational impacts and potential for effective human-AI collaboration. Overall, our study contributes three major advancements to the field:
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However, implementing AES systems effectively in real-world educational scenarios presents several challenges. First, the diverse range of exercise contexts and the inherent ambiguity in scoring rubrics complicate the ability of traditional models to deliver accurate scores. Second, interviews with high school teachers indicate that despite receiving accurate score predictions, they must still review essays to mitigate potential errors from the models. Consequently, relying exclusively on this system without human supervision is impractical in real-world scenarios. Thus, there is a clear need for AES systems that not only predict scores accurately but also facilitate effective human-AI collaboration. This should be supported by natural language explanations and additional assistive features to enhance usability.