Effects of Using Artificial Intelligence Teaching System for Environmental Education on Environmental Knowledge and Attitude

Huang, Shien-Ping

doi:10.29333/ejmste/91248

Cited by 31 publications

(18 citation statements)

References 25 publications

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“…The main characteristics of AI, described in all five studies, are the parallels between the human brain and artificial intelligence. The authors conceptualise AI as intelligent computer systems or intelligent agents with human features, such as the ability to memorise knowledge, to perceive and manipulate their environment in a similar way as humans, and to understand human natural language (see Huang, 2018;Lodhi, Mishra, Jain, & Bajaj, 2018;Welham, 2008). Dodigovic (2007) defines AI in her article as follows (p. 100):…”

Section: Understanding Of Ai and Critical Reflection Of Challenges Anmentioning

confidence: 99%

“…Teaching course content The disciplines that are taught through adaptive systems are diverse, including environmental education (Huang, 2018), animation design (Yuanyuan & Yajuan, 2014), language learning (Jia, 2009;Vlugter et al, 2009), Computer Science (Iglesias, Martinez, Aler, & Fernandez, 2009) and Biology (Chaudhri et al, 2013). Walsh, Tamjidul, and Williams (2017), however, present an adaptive system based on machine learning-human machine learning symbiosis from a descriptive perspective, without specifying any discipline.…”

Section: Assessment and Evaluationmentioning

confidence: 99%

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Systematic review of research on artificial intelligence applications in higher education – where are the educators?

Zawacki‐Richter

Marín

Bond

et al. 2019

Int J Educ Technol High Educ

1,581

468

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According to various international reports, Artificial Intelligence in Education (AIEd) is one of the currently emerging fields in educational technology. Whilst it has been around for about 30 years, it is still unclear for educators how to make pedagogical advantage of it on a broader scale, and how it can actually impact meaningfully on teaching and learning in higher education. This paper seeks to provide an overview of research on AI applications in higher education through a systematic review. Out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis, according to explicit inclusion and exclusion criteria. The descriptive results show that most of the disciplines involved in AIEd papers come from Computer Science and STEM, and that quantitative methods were the most frequently used in empirical studies. The synthesis of results presents four areas of AIEd applications in academic support services, and institutional and administrative services: 1. profiling and prediction, 2. assessment and evaluation, 3. adaptive systems and personalisation, and 4. intelligent tutoring systems. The conclusions reflect on the almost lack of critical reflection of challenges and risks of AIEd, the weak connection to theoretical pedagogical perspectives, and the need for further exploration of ethical and educational approaches in the application of AIEd in higher education.

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Section: Understanding Of Ai and Critical Reflection Of Challenges Anmentioning

confidence: 99%

Section: Assessment and Evaluationmentioning

confidence: 99%

Systematic review of research on artificial intelligence applications in higher education – where are the educators?

Zawacki‐Richter

Marín

Bond

et al. 2019

Int J Educ Technol High Educ

1,581

468

View full text Add to dashboard Cite

show abstract

“…The origin of machine translation can be traced back to the end of the ninth century when Arab cryptologists developed methods of frequency analysis, probability and statistical information, cryptanalysis, and translation into other system languages used in modern machine translation [ 15 ]. Although the idea of machine translation originated in the late seventeenth century and the 1920s, [ 16 ] proposed a common language in which the same idea is divided into a symbol. In 1956, the first machine translation conference marked a new stage of machine translation research.…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence-Based Translation Technology in Translation Teaching

Ling-hui

2022

Computational Intelligence and Neuroscience

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With the advent of the era of artificial intelligence revolution, deep learning is undoubtedly injecting new vitality into machine translation technology. In the context of artificial intelligence, teachers must find new and effective teaching ideas to improve the actual effect of translation teaching, which has drawn the attention of university teachers in recent years. With the development of society and the change of the times, English is becoming one of the implicit requirements of society for talents. Not only in the context of the new changes in curriculum, English teaching in schools and teachers is no longer limited to examinations, but more importantly, it is also significant to cultivate students’ comprehensive English ability so that they can really use their English knowledge and solve practical problems in life. On this basis, this paper investigates the practice of teaching English in college in the context of artificial intelligence.

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“…Next, the model identifies AI as another learning agent, shifting its nature and role from a mere learning tool. Previous studies take note of AI's human-resemblance characteristics as a unique feature that distinguishes it from traditional educational tools (Huang, 2018), and expand the role of AI in learning (Simmler & Frischknecht 2020). For example, AI could be a teacher to properly diagnose learning processes and outcomes, provide personalized feedback and evaluate achievement (Chaudhry & Kazim, 2021).…”

Section: Student-ai Collaborationmentioning

confidence: 99%

Learning design to support student-AI collaboration: perspectives of leading teachers for AI in education

2022

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Preparing students to collaborate with AI remains a challenging goal. As AI technologies are new to K-12 schools, there is a lack of studies that inform how to design learning when AI is introduced as a collaborative learning agent to classrooms. The present study, therefore, aimed to explore teachers’ perspectives on what (1) curriculum design, (2) student-AI interaction, and (3) learning environments are required to design student-AI collaboration (SAC) in learning and (4) how SAC would evolve. Through in-depth interviews with 10 Korean leading teachers in AI in Education (AIED), the study found that teachers perceived capacity and subject-matter knowledge building as the optimal learning goals for SAC. SAC can be facilitated through interdisciplinary learning, authentic problem solving, and creative tasks in tandem with process-oriented assessment and collaboration performance assessment. While teachers expressed instruction on AI principles, data literacy, error analysis, AI ethics, and AI experiences in daily life were crucial support, AI needs to offer an instructional scaffolding and possess attributes as a learning mate to enhance student-AI interaction. In addition, teachers highlighted systematic AIED policy, flexible school system, the culture of collaborative learning, and a safe to fail environment are significant. Teachers further anticipated students would develop collaboration with AI through three stages: (1) learn about AI, (2) learn from AI, and (3) learn together. These findings can provide a more holistic understanding of the AIED and implications for the educational policies, educational AI design as well as instructional design that are aimed at enhancing SAC in learning.

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Effects of Using Artificial Intelligence Teaching System for Environmental Education on Environmental Knowledge and Attitude

Cited by 31 publications

References 25 publications

Systematic review of research on artificial intelligence applications in higher education – where are the educators?

Systematic review of research on artificial intelligence applications in higher education – where are the educators?

Artificial Intelligence-Based Translation Technology in Translation Teaching

Learning design to support student-AI collaboration: perspectives of leading teachers for AI in education

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