Recently, ChatGPT, along with DALL-E-2 and Codex,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.

pdf/a-comprehensive-survey-of-ai-generated-content-aigc-a-1y36hmoe.pdf

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

In the educational setting, artificial intelligence (AI) technology, notably chatbots, has made substantial improvements in English learning. This study aims to determine the effectiveness of using the Artificial Intelligence Virtual Dream Friend and John English Boot applications on learning English in the 5.0 revolution era in English courses for first-semester students at university. The assessment method used is a quantitative research method and research design (quasi-experiment design). Based on the results of the study, it can be concluded that the results of the comparison test showed that My Virtual Dream Friend and John English Bot were both effective for use as computer tutoring in English courses and also increased interest in learning English in the 5.0 revolution era compared to previous conventional methods. The outcomes of this study might be used to direct future research into utilizing chatbots outside of the classroom as learning companions, and educators could use them to adapt evaluation and feedback procedures. 

AI-Aided Teaching Model in Education 5.0

Artificial intelligence-generated content (AIGC) refers to the use of AI to automate the information creation process while fulfilling the personalized requirements of users. However, due to the instability of AIGC models - for example, the stochastic nature of diffusion models - the quality and accuracy of the generated content can vary significantly. In wireless edge networks, the transmission of incorrectly generated content may unnecessarily consume network resources. Thus, a dynamic AIGC service provider (ASP) selection scheme is required to enable users to connect to the most suited ASP, improving the users' satisfaction as well as the quality of generated content. In this article, we first review the AIGC techniques and their applications in wireless networks. We then present the AIGC-as-a-service (AaaS) concept and discuss the challenges in deploying AaaS at the edge networks. It is essential to have performance metrics to evaluate the accuracy of AIGC services. Thus, we introduce several image-based perceived quality evaluation metrics. Then, we propose a general and effective model to illustrate the relationship between computational resources and user-perceived quality evaluation metrics. To achieve efficient AaaS and maximize the quality of generated content in wireless edge networks, we propose a deep reinforcement learning-enabled algorithm for optimal ASP selection. Simulation results show that the proposed algorithm can provide a higher quality of generated content to users and achieve fewer crashed tasks by comparing with four benchmarks, that is, overloading-avoidance, randomness, round-robin policies, and the upper-bound schemes.

Enabling AI-Generated Content Services in Wireless Edge Networks

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. 

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

This study investigated the perceptions of Vietnamese tertiary-level English as a Foreign Language (EFL) teachers regarding the use of ChatGPT, an advanced artificial intelligence (AI) language model, in students’ fairy tale retelling writing tasks. Employing a qualitative methodology, the research involved semi-structured interviews with nine EFL teachers from two Vietnamese institutions representing a range of teaching experiences: novice, mid-career, and near-end-career. The technology acceptance model (TAM) and constructivist learning theory (CLT) framed the study, providing a dual perspective on technology integration in language education. The thematic analysis revealed a spectrum of challenges and opportunities in integrating ChatGPT into language teaching. Key challenges include concerns about over-reliance on AI, cultural and contextual misalignments, integration with existing teaching methods, language accuracy issues, impact on creativity, technical barriers, and ethical considerations. Conversely, opportunities identified encompass enhanced student engagement, personalized learning, professional development for teachers, improvement in language proficiency, reduction in teacher workload, encouragement of technological adoption, and fostering of critical thinking and creativity. The study suggests a need for balanced AI integration in language education, emphasizing personalized, interactive learning experiences and professional development for teachers.

Exploring the Impact of AI in Language Education: Vietnamese EFL Teachers’ Views on Using ChatGPT for Fairy Tale Retelling Tasks

Artificial intelligence (AI) technologies have the potential to reduce the workload for the second language (L2) teachers and test developers. We propose two AI distractor-generating methods for creating Chinese vocabulary items: semantic similarity and visual similarity. Semantic similarity refers to antonyms and synonyms, while visual similarity refers to the phenomenon that two phrases share one or more characters in common. This study explores the construct validity of the two types of selected-response vocabulary tests (AI-generated items and human expert-designed items) and compares their item difficulty and item discrimination. Both quantitative and qualitative data were collected. Seventy-eight students from Beijing Language and Culture University were asked to respond to AI-generated and human expert-designed items respectively. Students’ scores were analyzed using the two-parameter item response theory (2PL-IRT) model. Thirteen students were then invited to report their test taking strategies in the think-aloud section. The findings from the students’ item responses revealed that the human expert-designed items were easier but had more discriminating power than the AI-generated items. The results of think-aloud data indicated that the AI-generated items and expert-designed items might assess different constructs, in which the former elicited test takers’ bottom-up test-taking strategies while the latter seemed more likely to trigger test takers’ rote memorization ability.

https://journals.sagepub.com/doi/pdf/10.1177/21582440221082130

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

Generative AI technology and language learning: global language learners’ responses to ChatGPT videos in social media

Feedback engagement as a multidimensional construct: a validation study

The benefits of using social annotations to promote collaborative learning experiences have been investigated in the context of digital reading and listening, while its implications in multimedia learning contexts remain unclear. To investigate the influence of social annotations on multimedia language learning, we invited 16 African participants who were learning Chinese as a second language through a MOOC platform in China. They were given full autonomy to study and to shift instructional videos between three modes: no visual aids, linguistic captions, and social annotations. Guided by self-determination theory, the qualitative data were collected through one-to-one interviews, while quantitative data, such as the duration of watching and the number of questions raised by participants, were obtained from observations. Statistical analysis suggests that MOOC learners demonstrated a higher level of engagement and motivation in the social annotation mode than in the other two modes. These statistical findings can be explained from the following three perspectives: content and emotion-related social annotations by other L1 viewers can assist in (1) checking their comprehension of the video by comparing their understanding with others; (2) adjusting learning goals at both the linguistic and content level and therefore using self-regulated learning strategies to a greater extent, especially in actively seeking assistance and other learning resources, and (3) relating the video content to the teaching curriculum/personal experience/learning goals, therefore creating a sense of belonging to a larger language learner community as language learners, interpreters and contributors to the video social annotations. 

Social annotations and second language viewers’ engagement with multimedia learning resources in LMOOCs: a self-determination theory perspective

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

Generative AI technology and language learning: global language learners’ responses to ChatGPT videos in social media

Feedback engagement as a multidimensional construct: a validation study

Social annotations and second language viewers’ engagement with multimedia learning resources in LMOOCs: a self-determination theory perspective