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教育中的大语言模型和生成式人工智能:尼日利亚在职教师通过混合人工神经网络-偏最小二乘法-结构方程模型方法得出的见解

Large language models and GenAI in education: Insights from Nigerian in-service teachers through a hybrid ANN-PLS-SEM approach.

作者信息

Ayanwale Musa Adekunle, Adelana Owolabi Paul, Bamiro Nurudeen Babatunde, Olatunbosun Stella Oluwakemi, Idowu Kabir Oluwatobi, Adewale Kayode A

机构信息

Department of Mathematics, Science and Technology Education, Faculty of Education, University of Johannesburg, Auckland Park, South Africa.

Institute of Educational Technology, The Open University, Milton Keynes, UK.

出版信息

F1000Res. 2025 Mar 4;14:258. doi: 10.12688/f1000research.161637.1. eCollection 2025.

Abstract

BACKGROUND

The rapid integration of Artificial Intelligence (AI) in education offers transformative opportunities to enhance teaching and learning. Among these innovations, Large Language Models (LLMs) like ChatGPT hold immense potential for instructional design, personalized learning, and administrative efficiency. However, integrating these tools into resource-constrained settings such as Nigeria presents significant challenges, including inadequate infrastructure, digital inequities, and teacher readiness. Despite the growing research on AI adoption, limited studies focus on developing regions, leaving a critical gap in understanding how educators perceive and adopt these technologies.

METHODS

We adopted a hybrid approach, combining Partial Least Squares Structural Equation Modelling (PLS-SEM) and Artificial Neural Networks (ANN) to uncover both linear and nonlinear dynamics influencing behavioral intention (BI) of 260 Nigerian in-service teachers regarding ChatGPT after participating in structured training. Key predictors examined include Perceived Ease of Use (PEU), Perceived Usefulness (PUC), Attitude Towards ChatGPT (ATC), Your Colleagues and Your Use of ChatGPT (YCC), Technology Anxiety (TA), Teachers' Trust in ChatGPT (TTC), and Privacy Issues (PIU).

RESULTS

Our PLS-SEM results highlight PUC, TA, YCC, and PEU, in that order of importance, as significant predictors, explaining 15.8% of the variance in BI. Complementing these, ANN analysis identified PEU, ATC, and PUC as the most critical factors, demonstrating substantial predictive accuracy with an RMSE of 0.87. This suggests that while PUC drives adoption, PEU and positive attitudes are foundational in fostering teacher engagement with AI technologies.

CONCLUSION

Our results highlight the need for targeted professional development initiatives to enhance teachers' digital competencies, reduce technology-related anxiety, and build trust in AI tools like ChatGPT. Our study offers actionable insights for policymakers and educational stakeholders, emphasizing the importance of fostering an inclusive and ethical AI ecosystem. We aim to empower teachers and support AI-driven educational transformation in resource-limited environments by addressing contextual barriers.

摘要

背景

人工智能(AI)在教育领域的迅速整合为提升教学提供了变革性机遇。在这些创新成果中,像ChatGPT这样的大语言模型(LLMs)在教学设计、个性化学习和管理效率方面具有巨大潜力。然而,将这些工具整合到像尼日利亚这样资源有限的环境中面临重大挑战,包括基础设施不足、数字不平等以及教师准备情况不佳。尽管关于AI采用的研究不断增加,但针对发展中地区的研究有限,在理解教育工作者如何看待和采用这些技术方面存在关键差距。

方法

我们采用了一种混合方法,结合偏最小二乘结构方程模型(PLS - SEM)和人工神经网络(ANN),以揭示影响260名参与结构化培训后的尼日利亚在职教师对ChatGPT的行为意向(BI)的线性和非线性动态。考察的关键预测因素包括感知易用性(PEU)、感知有用性(PUC)、对ChatGPT的态度(ATC)、你的同事与你对ChatGPT的使用(YCC)、技术焦虑(TA)、教师对ChatGPT的信任(TTC)以及隐私问题(PIU)。

结果

我们的PLS - SEM结果突出显示,按重要性排序,PUC、TA、YCC和PEU是显著的预测因素,解释了BI中15.8%的方差。作为补充,ANN分析确定PEU、ATC和PUC是最关键的因素,以0.87的均方根误差(RMSE)展示了较高的预测准确性。这表明虽然PUC推动采用,但PEU和积极态度是促进教师参与AI技术的基础。

结论

我们的结果凸显了开展有针对性的专业发展举措的必要性,以提升教师的数字能力、减少与技术相关的焦虑,并建立对ChatGPT等AI工具的信任。我们的研究为政策制定者和教育利益相关者提供了可操作的见解,强调了培育包容且符合道德规范的AI生态系统的重要性。我们旨在通过解决背景障碍,赋能教师并支持资源有限环境中由AI驱动的教育变革。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b38d/12359025/d15498a89d3f/f1000research-14-177692-g0000.jpg

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