Research Article
Luis Eduardo Muñoz Guerrero, Yony Fernando Ceballos, Luis David Trejos Rojas
CONT ED TECHNOLOGY, Volume 17, Issue 3, Article No: ep582
ABSTRACT
Recent progress made in conversational AI lays emphasis on the need for development of language models that possess solid logical reasoning skills and further extrapolated capabilities. An examination into this phenomenon investigates how well the Capybara dataset can improve one’s ability to reason using language-based systems. Multiple cutting-edge linguistic models were fine-tuned using the Capybara corpus before assessing their performances on standard tasks demanding sophisticated reasoning. The comparison using different ways reveals that the logical reasoning of models improves and their ability to make inferences is enhanced. This research explores this further by considering what it means for developers who want more human-like machine conversation intelligence. We also see that this could become an invaluable tool when training reasoning-oriented language generating models.
Keywords: logical reasoning, language models, Capybara dataset, fine-tuning, extrapolation, conversational AI
Research Article
Mohamed Ali Elkot, Eltaieb Youssif, Omer Elsheikh Hago Elmahdi, Mohammed AbdAlgane, Rabea Ali
CONT ED TECHNOLOGY, Volume 17, Issue 1, Article No: ep549
ABSTRACT
Utilizing artificial intelligence (AI) technology in educational institutions for students with mild intellectual disabilities offers promising avenues for enhancing this population’s learning outcomes and skill development. This study aims to investigate the effect of using generative conversational AI to improve English communication skills among students with mild intellectual disabilities. The study involved twelve students diagnosed with mild intellectual disabilities, divided equally into two groups. Six students engaged in guided conversations with AI, while the other six participated in free conversations with AI. These participants were randomly chosen from educational institutions specializing in intellectual disability education and mainstream schools. The results indicate that guided conversations significantly enhance English communication skills among participants. Additionally, the study highlights the development gains from engaging in guided conversations by AI applications. Statistical analysis reveals notable differences in the effect of guided versus free conversational approaches, with guided conversations yielding superior outcomes. This underscores the importance of structured guidance for comprehension and participation in different English communication skills among students with mild intellectual disabilities. Moreover, the study recommends the integration of AI tools in education to support students with disabilities, emphasizing the need for tailored AI applications to cater to diverse learning needs.
Keywords: conversational AI, intellectual disability, education, communication skills