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From Text to Moving Image: Evaluating Generative Artificial Intelligence Text-to-Video Models for Pre-Writing Idea Generation in Language Instruction

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Title: From Text to Moving Image: Evaluating Generative Artificial Intelligence Text-to-Video Models for Pre-Writing Idea Generation in Language Instruction
Language: English
Authors: Rong Yaojun (ORCID 0009-0004-0069-9672), Kizito Tekwa (ORCID 0000-0003-2886-9106)
Source: Education and Information Technologies. 2025 30(13):18749-18778.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 30
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Artificial Intelligence, Technology Uses in Education, Captions, Video Technology, Prewriting, Concept Formation, Computer Mediated Communication, Natural Language Processing, Brainstorming, Descriptive Writing
DOI: 10.1007/s10639-025-13516-6
ISSN: 1360-2357
1573-7608
Abstract: Research on using generative artificial intelligence (GenAI) chatbots in learning environments continues to underpin research in and out of academia. However, GenAI text-to-video (T2V) models have not been adequately investigated in academia, mostly due to their novelty. That means their potential to improve learning remains to be determined. This study contributes to bridging this gap by investigating how GenAI T2V models can foster idea generation during brainstorming. The participants are two groups (experiment and control) of college freshmen, with one group presented with AI-generated videos during brainstorming. Data gathered from the participants' screen recordings, individual notes, a questionnaire, and an assessment of brainstormed ideas to determine whether the participants in the experiment group benefited from the background and narrative elements of the AI-generated videos showed that more ideas were generated within a shorter time, and the videos were perceived to be useful and of high quality. Though the findings did not overwhelmingly support an empirically informed case for deploying GenAI T2V models in descriptive writing, they have significant implications for teachers, students, and institutions of higher learning.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1480844
Database: ERIC
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: Research on using generative artificial intelligence (GenAI) chatbots in learning environments continues to underpin research in and out of academia. However, GenAI text-to-video (T2V) models have not been adequately investigated in academia, mostly due to their novelty. That means their potential to improve learning remains to be determined. This study contributes to bridging this gap by investigating how GenAI T2V models can foster idea generation during brainstorming. The participants are two groups (experiment and control) of college freshmen, with one group presented with AI-generated videos during brainstorming. Data gathered from the participants' screen recordings, individual notes, a questionnaire, and an assessment of brainstormed ideas to determine whether the participants in the experiment group benefited from the background and narrative elements of the AI-generated videos showed that more ideas were generated within a shorter time, and the videos were perceived to be useful and of high quality. Though the findings did not overwhelmingly support an empirically informed case for deploying GenAI T2V models in descriptive writing, they have significant implications for teachers, students, and institutions of higher learning.
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      – SubjectFull: Captions
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      – TitleFull: From Text to Moving Image: Evaluating Generative Artificial Intelligence Text-to-Video Models for Pre-Writing Idea Generation in Language Instruction
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