Quiz Maker: Automatic Quiz Generation from Text Using NLP

Gabajiwala, Ebrahim; Mehta, Priyav; Singh, Rajneesh; Koshy, Reeta

doi:10.1007/978-981-19-5037-7_37

Cited by 18 publications

(8 citation statements)

References 19 publications

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“…One example is in the creation and design of educational content. For example, researchers have used large language models to generate interactive educational materials such as quizzes and flashcards, which can be used to improve student learning and engagement [16,17]. More specifically, in a recent work by Dijkstra et al [17], researchers have used GPT-3 to generate multiple-choice questions and answers for a reading comprehension task and argue that automated generation of quizzes not only reduces the burden of manual quiz design for teachers but, above all, provides a helpful tool for students to train and test their knowledge while learning from textbooks and during exam preparation [17].…”

Section: Review Of Research Applying Large Languagementioning

confidence: 99%

ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education

Kasneci¹,

Seßler²,

Küchemann³

et al. 2023

Preprint

124

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Large language models represent a significant advancement in the field of AI. The underlying technology is key to further innovations and, despite critical views and even bans within communities and regions, large language models are here to stay. This position paper presents the potential benefits and challenges of educational applications of large language models, from student and teacher perspectives. We briefly discuss the current state of large language models and their applications. We then highlight how these models can be used to create educational content, improve student engagement and interaction, and personalize learning experiences. With regard to challenges, we argue that large language models in education require teachers and learners to develop sets of competencies and literacies necessary to both understand the technology as well as their limitations and unexpected brittleness of such systems. In addition, a clear strategy within educational systems and a clear pedagogical approach with a strong focus on critical thinking and strategies for fact checking are required to integrate and take full advantage of large language models in learning settings and teaching curricula. Other challenges such as the potential bias in the output, the need for continuous human oversight, and the potential for misuse are not unique to the application of AI in education. But we believe that, if handled sensibly, these challenges can offer insights and opportunities in education scenarios to acquaint students early on with potential societal biases, criticalities, and risks of AI applications. We conclude with recommendations for how to address these challenges and ensure that such models are used in a responsible and ethical manner in education.

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Section: Review Of Research Applying Large Languagementioning

confidence: 99%

ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education

Kasneci¹,

Seßler²,

Küchemann³

et al. 2023

Preprint

124

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show abstract

“…Recently, numerous researchers have endeavored to incorporate LLMs into educational technologies [51]. These adaptations have seen the utilization of LLMs in creating educational content [19,29], enhancing student engagement and interaction [1,7,88], and personalizing learning experiences [81]. For instance, LLMs have been used to generate children's narratives [4,35], some of which have even been sold publicly [64].…”

Section: Using Large Language Models For Child-facing Conversational ...mentioning

confidence: 99%

Mathemyths: Leveraging Large Language Models to Teach Mathematical Language through Child-AI Co-Creative Storytelling

Zhang,

Liu,

Ziska

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Figure 1: Interaction between a child and Mathemyths: demonstrating the system's ability to teach mathematical language through child-AI co-creative storytelling. Mathemyths provides open-ended questions to solicit how the child wishes the story should progress, on-the-fly feedback to acknowledge the child's responses, and co-creative story continuation with in-context explanations of math words. When the child needs additional support to continue the story, Mathemyths offers scaffolding through follow-up questions and "hint & rephrase" strategies.

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“…A few of the opportunities/strengths and weaknesses/challenges associated with GenAI tools as noted in the surveyed literature are outlined below. They can be used for [4,6,81,84,[90][91][92][93] • Generation of large-scale personalized assessment • Generation of targeted practice problems from course materials • Automatic quiz generation tailored to a student's level of knowledge • Provision of explanation of complex texts to ease understanding of study materials • Provision of real-time and personalized feedback for assessment • Development of an inclusive learning environment, for example through integration with textto-speech or speech-to-text facilities • Enhancement of the development of domain-specific skills such as programming • Help in the editing of writing tasks • Text mining (e.g., legal documents, medical reports, etc.) • Provision of assistance to teachers in stages of lecture preparation, delivery, and assessment • Deployment as virtual assistant for technical support in service-oriented businesses…”

Section: Opportunities For Learning Teaching and Researchmentioning

confidence: 99%

Bard, ChatGPT and 3DGPT: A Scientometric Analysis of Generative AI Tools and Assessment of Implications for Mechanical Engineering Education

Mustapha,

Yap,

Abakr

2024

Preprint

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Following the recent rise in generative artificial intelligence (GenAI) tools, fundamental questions about their wider impacts have started reverberating around various disciplines. To this end, this work was undertaken: (i) firstly, to track the unfolding landscape of general issues surrounding GenAI tools; (ii) secondly, as an exploratory inquiry to elucidate the specific opportunities and limitations of GenAI tools as part of the technology-assisted enhancement of mechanical engineering education and professional practices. As part of the investigation, we conduct and present a brief scientometric analysis of recently published studies to unravel the emerging trend on the subject matter. Further, experimentation was done with selected GenAI tools (Bard, ChatGPT, DALL.E, and 3DGPT) for mechanical engineering-related tasks. The study identified several pedagogical and professional opportunities and guidelines for deploying GenAI tools in mechanical engineering. Besides, the study highlights some pitfalls of GenAI tools for analytical reasoning tasks (e.g., subtle errors in computation involving unit conversions) and sketching/image generation tasks (e.g., poor demonstration of symmetry).

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Quiz Maker: Automatic Quiz Generation from Text Using NLP

Cited by 18 publications

References 19 publications

ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education

ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education

Mathemyths: Leveraging Large Language Models to Teach Mathematical Language through Child-AI Co-Creative Storytelling

Bard, ChatGPT and 3DGPT: A Scientometric Analysis of Generative AI Tools and Assessment of Implications for Mechanical Engineering Education

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