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Yu-Shan Chang; Meng-Chen Tsai – Educational Studies, 2024
This study explored the effects of design thinking on the conceptual cognition of artificial intelligence (AI) learning, attitudes toward AI, idea creativity, and the product creativity of AI applications. The concept map indicated that design thinking had a significant effect on the conceptual cognition of AI learning, particularly the relational…
Descriptors: Design, Cognitive Processes, Creativity, Artificial Intelligence
Hunkoog Jho; Minsu Ha – Journal of Baltic Science Education, 2024
This study aimed at examining the performance of generative artificial intelligence to extract argumentation elements from text. Thus, the researchers developed a web-based framework to provide automated assessment and feedback relying on a large language model, ChatGPT. The results produced by ChatGPT were compared to human experts across…
Descriptors: Feedback (Response), Artificial Intelligence, Persuasive Discourse, Models
José Luís Araújo; Isabel Saúde – Journal of Chemical Education, 2024
The rapid evolution of Artificial Intelligence (AI) is profoundly shaping our society. Among various AI tools, ChatGPT stands out for its user-friendly nature and wide accessibility to the public. However, despite their countless potential benefits, these tools also face significant challenges, especially in sensitive areas like Education. In this…
Descriptors: Artificial Intelligence, Technology Uses in Education, Natural Language Processing, Chemistry
Yi Gui – ProQuest LLC, 2024
This study explores using transfer learning in machine learning for natural language processing (NLP) to create generic automated essay scoring (AES) models, providing instant online scoring for statewide writing assessments in K-12 education. The goal is to develop an instant online scorer that is generalizable to any prompt, addressing the…
Descriptors: Writing Tests, Natural Language Processing, Writing Evaluation, Scoring
Odicar Joice Chavez; Thelma Palaoag – Journal of Research in Innovative Teaching & Learning, 2024
Purpose: This study investigates user preferences for motivational features aligned with self-determination theory (SDT), emphasizing autonomy, relatedness, and competency. The study seeks to identify the most appealing and effective motivational features in AI-driven mobile apps for fostering autonomy, promoting relatedness, and enhancing…
Descriptors: Artificial Intelligence, Computer Oriented Programs, Handheld Devices, Telecommunications