# Experiences with Content Development and Assessment Design in the Era of GenAI

arXiv: https://arxiv.org/abs/2503.00081 · HTML: https://mlsanity.com/paper/2503.00081/

## Key facts

- Cited by 1 papers 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 (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021)
- [From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy](https://mlsanity.com/paper/2307.00691/) (2023)
- [LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning](https://mlsanity.com/paper/2401.16185/) (2024)
- [How Can Large Language Models Help Humans in Design and Manufacturing?](https://mlsanity.com/paper/2307.14377/) (2023)
- [Combining Fine-Tuning and LLM-based Agents for Intuitive Smart Contract Auditing with Justifications](https://mlsanity.com/paper/2403.16073/) (2024)
- [Fine-tuning and Utilization Methods of Domain-specific LLMs](https://mlsanity.com/paper/2401.02981/) (2024)
- [A Comprehensive AI Policy Education Framework for University Teaching and Learning](https://mlsanity.com/paper/2305.00280/) (2023)
- [ChatGPT: Applications, Opportunities, and Threats](https://mlsanity.com/paper/2304.09103/) (2023)
- [Identifying Untrustworthy Samples: Data Filtering for Open-domain Dialogues with Bayesian Optimization](https://mlsanity.com/paper/2109.06471/) (2021)
- [Framework for Adoption of Generative Artificial Intelligence (GenAI) in Education](https://mlsanity.com/paper/2408.01443/) (2024)
- [Crafting Tomorrow's Evaluations: Assessment Design Strategies in the Era of Generative AI](https://mlsanity.com/paper/2405.01805/) (2024)

## Built on by (most cited first, 1 of 1)

- [Navigating the New Landscape: A Conceptual Model for Project-Based Assessment (PBA) in the Age of GenAI](https://mlsanity.com/paper/2508.11709/) (2025)

## 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.