Artificial Intelligence-Generated and Human Expert-Designed Vocabulary Tests: A Comparative Study

Yunjiu, Luo; Wei, Wei; Zheng, Ying

doi:10.1177/21582440221082130

Cited by 16 publications

(13 citation statements)

References 44 publications

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“…During the research, we investigate explicit indicators of CPS, such as commognitive conflict categories, the occurrence and the average conflict duration etc., which are conducive to automatic identification and data analysis in the context of the rapid development of artificial intelligence (AI) for speech recognition. As AI technologies have the potential to reduce the workload for teachers and test developers ( Yunjiu et al, 2022 ), their further development in the application of commognitive theory can provide directions and scripts for AI-assisted teaching, particularly in commognitive conflict discourse recognition and diagnosis in the future.…”

Section: Discussionmentioning

confidence: 99%

Visual analysis of commognitive conflict in collaborative problem solving in classrooms

Lu,

Zhang,

2023

Front. Psychol.

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In today’s knowledge-intensive and digital society, collaborative problem-solving (CPS) is considered a critical skill for students to develop. Moreover, international education research has embraced a new paradigm of communication-focused inquiry, and the commognitive theory helps enhance the understanding of CPS work. This paper aims to enhance the CPS skills by identifying, diagnosing, and visualizing commognitive conflicts during the CPS process, thereby fostering a learning-oriented innovative approach and even giving the script of technology-assisted feedback practices. Specifically, we utilized open-ended mathematical tasks and multi-camera video recordings to analyze the commognitive conflicts in CPS among 32 pairs, comprising 64 Year 7 students. After selecting the high-quality, medium-quality, and low-quality student pairs based on the SOLO theory, further investigations were made in the discourse diagnosis and visual analysis for the knowledge dimensions of commognitive conflict. Finally, it was discovered that there is a need to encourage students to focus on and resolve commognitive conflicts while providing timely feedback. Visual studies of commognitive conflict can empower AI-assisted teaching, and the intelligent diagnosis and visual analysis of CPS provide innovative solutions for teaching feedback.

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Section: Discussionmentioning

confidence: 99%

Visual analysis of commognitive conflict in collaborative problem solving in classrooms

Lu,

Zhang,

2023

Front. Psychol.

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“…Combining data from sources [1,3,[33][34][35][36][37][38][39][40]46,47], we can state that AI has become an integral and essential tool for achieving SDG 4.3. Today, AI tools quickly adapt educational programs to the individual needs of "learners".…”

Section: General Theoretical Frameworkmentioning

confidence: 99%

Will AI Become a Threat to Higher Education Sustainability? A Study of Students’ Views

Okulich-Kazarin,

Artyukhov,

Skowron

et al. 2024

Sustainability

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Universities started to use artificial intelligence (AI) tools to improve the quality of higher education services. However, the rapid adoption of AI tools in higher education (HE) may lead to sustainability issues. On the one hand, there are prerequisites for using AI tools to achieve Sustainable Development Goal 4 (SDG 4). On the other hand, as consumers of educational services (stakeholders), students have their own opinions about using AI in the educational process. The purpose of this study was to explore students’ opinions on the use of artificial intelligence tools in higher education. The authors analyzed student responses to the question: “Do you think AI threatens higher education in the next five years?” The authors formulated this question based on the definition of “a safe learning environment”, which is associated with a “safe” learning environment (SDG 4.3). The authors made use of a literature review, a bibliometric analysis of 5000 sources, a survey of 1104 students from eight universities in Eastern Europe through cloud technologies to host a special electronic questionnaire, statistical processing of questionnaires, and testing of statistical hypotheses. The authors formulated and tested two pairs of competing statistical hypotheses. Finally, the authors obtained three new scientific facts based on the respondents’ answers. New scientific facts were obtained using a standard level of statistical hypothesis testing (α = 0.05). The main scientific fact is that 10.17% to 35.42% of students think that Artificial Intelligence threatens higher education. According to student opinions, AI may hurt the sustainability of higher education (SDG 4.3). The authors are confident that new scientific facts help conceptualize and promote didactic theory and practice. The study results are needed to predict, plan, and implement organizational, pedagogical, and methodological measures aimed at SDG 4.3 through a “safe” learning environment while further expanding the use of AI in higher education.

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“…In recent years, Artificial Intelligence Generated Content (AIGC) has gained much attention beyond the computer science community, where the whole society begins to be interested in the various content generation products built by large tech companies [3], such as ChatGPT [4] and DALL-E-2 [5]. AIGC refers to content that is generated using advanced Generative AI (GAI) techniques, as opposed to being created by human authors, which can automate the creation of large amounts of content in a short amount of time.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT

Cao¹,

Li²,

Liu³

et al. 2023

Preprint

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Recently, ChatGPT, along with DALL-E-2 [1] and Codex [2],has been gaining significant attention from society. As a result, many individuals have become interested in related resources and are seeking to uncover the background and secrets behind its impressive performance. In fact, ChatGPT and other Generative AI (GAI) techniques belong to the category of Artificial Intelligence Generated Content (AIGC), which involves the creation of digital content, such as images, music, and natural language, through AI models. The goal of AIGC is to make the content creation process more efficient and accessible, allowing for the production of high-quality content at a faster pace. AIGC is achieved by extracting and understanding intent information from instructions provided by human, and generating the content according to its knowledge and the intent information. In recent years, large-scale models have become increasingly important in AIGC as they provide better intent extraction and thus, improved generation results. With the growth of data and the size of the models, the distribution that the model can learn becomes more comprehensive and closer to reality, leading to more realistic and high-quality content generation. This survey provides a comprehensive review on the history of generative models, and basic components, recent advances in AIGC from unimodal interaction and multimodal interaction. From the perspective of unimodality, we introduce the generation tasks and relative models of text and image. From the perspective of multimodality, we introduce the cross-application between the modalities mentioned above. Finally, we discuss the existing open problems and future challenges in AIGC.

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Artificial Intelligence-Generated and Human Expert-Designed Vocabulary Tests: A Comparative Study

Cited by 16 publications

References 44 publications

Visual analysis of commognitive conflict in collaborative problem solving in classrooms

Visual analysis of commognitive conflict in collaborative problem solving in classrooms

Will AI Become a Threat to Higher Education Sustainability? A Study of Students’ Views

A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT

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