# Exploring the Capabilities of Prompted Large Language Models in Educational and Assessment Applications

arXiv: https://arxiv.org/abs/2405.11579 · HTML: https://mlsanity.com/paper/2405.11579/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 68 references that are in the map.
- First posted on arXiv 19 May 2024.

## Builds on (most cited first, 30 of 33)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [RACE: Large-scale ReAding Comprehension Dataset From Examinations](https://mlsanity.com/paper/1704.04683/) (2017)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [QuAC : Question Answering in Context](https://mlsanity.com/paper/1808.07036/) (2018)
- [NewsQA: A Machine Comprehension Dataset](https://mlsanity.com/paper/1611.09830/) (2016)
- [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://mlsanity.com/paper/1907.12461/) (2019)
- [Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks](https://mlsanity.com/paper/2010.08240/) (2020)
- [JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction](https://mlsanity.com/paper/1702.04066/) (2017)
- [The Robots are Here: Navigating the Generative AI Revolution in Computing Education](https://mlsanity.com/paper/2310.00658/) (2023)
- [What all do audio transformer models hear? Probing Acoustic Representations for Language Delivery and its Structure](https://mlsanity.com/paper/2101.00387/) (2021)
- [Prompted Opinion Summarization with GPT-3.5](https://mlsanity.com/paper/2211.15914/) (2022)
- [GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval](https://mlsanity.com/paper/2104.12741/) (2021)
- [Semantic Graphs for Generating Deep Questions](https://mlsanity.com/paper/2004.12704/) (2020)
- [Controllable Open-ended Question Generation with A New Question Type Ontology](https://mlsanity.com/paper/2107.00152/) (2021)
- [MSP: Multi-Stage Prompting for Making Pre-trained Language Models Better Translators](https://mlsanity.com/paper/2110.06609/) (2021)
- [MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2204.10994/) (2022)
- [Generative Language Models for Paragraph-Level Question Generation](https://mlsanity.com/paper/2210.03992/) (2022)
- [M2D2: A Massively Multi-domain Language Modeling Dataset](https://mlsanity.com/paper/2210.07370/) (2022)
- [Cross-Lingual Training for Automatic Question Generation](https://mlsanity.com/paper/1906.02525/) (2019)
- [Capturing Greater Context for Question Generation](https://mlsanity.com/paper/1910.10274/) (2019)

## Built on by (most cited first, 12 of 12)

- [Applicability of Large Language Models and Generative Models for Legal Case Judgement Summarization](https://mlsanity.com/paper/2407.12848/) (2024)
- [Generative AI and Its Impact on Personalized Intelligent Tutoring Systems](https://mlsanity.com/paper/2410.10650/) (2024)
- [How Effective is GPT-4 Turbo in Generating School-Level Questions from Textbooks Based on Bloom's Revised Taxonomy?](https://mlsanity.com/paper/2406.15211/) (2024)
- [Code Generation and Algorithmic Problem Solving Using Llama 3.1 405B](https://mlsanity.com/paper/2409.19027/) (2024)
- [The Future of Learning in the Age of Generative AI: Automated Question Generation and Assessment with Large Language Models](https://mlsanity.com/paper/2410.09576/) (2024)
- [MIRROR: A Novel Approach for the Automated Evaluation of Open-Ended Question Generation](https://mlsanity.com/paper/2410.12893/) (2024)
- [Human-Centric eXplainable AI in Education](https://mlsanity.com/paper/2410.19822/) (2024)
- [Prompt Engineering Using GPT for Word-Level Code-Mixed Language Identification in Low-Resource Dravidian Languages](https://mlsanity.com/paper/2411.04025/) (2024)
- [Small, Private Language Models as Teammates for Educational Assessment Design](https://mlsanity.com/paper/2605.15015/) (2026)
- [Cancer-Answer: Empowering Cancer Care with Advanced Large Language Models](https://mlsanity.com/paper/2411.06946/) (2024)
- [CryptoLLM: Unleashing the Power of Prompted LLMs for SmartQnA and Classification of Crypto Posts](https://mlsanity.com/paper/2411.07917/) (2024)
- [Optimizing Coverage and Difficulty in Reinforcement Learning for Quiz Composition](https://mlsanity.com/paper/2603.27695/) (2026)

## Abstract (arXiv)

In the era of generative artificial intelligence (AI), the fusion of large language models (LLMs) offers unprecedented opportunities for innovation in the field of modern education. We embark on an exploration of prompted LLMs within the context of educational and assessment applications to uncover their potential. Through a series of carefully crafted research questions, we investigate the effectiveness of prompt-based techniques in generating open-ended questions from school-level textbooks, assess their efficiency in generating open-ended questions from undergraduate-level technical textbooks, and explore the feasibility of employing a chain-of-thought inspired multi-stage prompting approach for language-agnostic multiple-choice question (MCQ) generation. Additionally, we evaluate the ability of prompted LLMs for language learning, exemplified through a case study in the low-resource Indian language Bengali, to explain Bengali grammatical errors. We also evaluate the potential of prompted LLMs to assess human resource (HR) spoken interview transcripts. By juxtaposing the capabilities of LLMs with those of human experts across various educational tasks and domains, our aim is to shed light on the potential and limitations of LLMs in reshaping educational practices.