Experiences with Content Development and Assessment Design in the Era of GenAI
Not on the map. Why?
Abstract (arXiv)
Generative Artificial Intelligence (GenAI) has the potential to transform higher education by generating human-like content. The advancement in GenAI has revolutionised several aspects of education, especially subject and assessment design. In this era, it is crucial to design assessments that challenge students and cannot be solved using GenAI tools. This makes it necessary to update the educational content with rapidly evolving technology. The assessment plays a significant role in ensuring the students learning, as it encourages students to engage actively, leading to the achievement of learning outcomes. The paper intends to determine how effectively GenAI can design a subject, including lectures, labs and assessments, using prompts and custom-based training. This paper aims to elucidate the direction to educators so they can leverage GenAI to create subject content. Additionally, we provided our experiential learning for educators to develop content, highlighting the importance of prompts and fine-tuning to ensure output quality. It has also been observed that expert evaluation is essential for assessing the quality of GenAI-generated materials throughout the content generation process.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 37 references that are in the map. First posted on arXiv 28 Feb 2025.
Builds on
17 of 37 references.
References · 17
- 2017Attention Is All You Needcited by 60,361
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
- 2022Survey of Hallucination in Natural Language Generationcited by 2,075
- 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,310
- 2021CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generationcited by 906
- 2023From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacycited by 143
- 2024LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoningcited by 59
- 2023How Can Large Language Models Help Humans in Design and Manufacturing?cited by 41
- 2024Combining Fine-Tuning and LLM-based Agents for Intuitive Smart Contract Auditing with Justificationscited by 41
- 2024Fine-tuning and Utilization Methods of Domain-specific LLMscited by 36
5 more
- 2023A Comprehensive AI Policy Education Framework for University Teaching and Learningcited by 34
- 2023ChatGPT: Applications, Opportunities, and Threatscited by 28
- 2021Identifying Untrustworthy Samples: Data Filtering for Open-domain Dialogues with Bayesian Optimizationcited by 15
- 2024Framework for Adoption of Generative Artificial Intelligence (GenAI) in Educationcited by 6
- 2024Crafting Tomorrow's Evaluations: Assessment Design Strategies in the Era of Generative AIcited by 2
Built on by
Citing papers per year: 2025: 1.
1 paper.