1,538 research outputs found
Collaboration scripts - a conceptual analysis
This article presents a conceptual analysis of collaboration scripts used in face-to-face and computer-mediated collaborative learning. Collaboration scripts are scaffolds that aim to improve collaboration through structuring the interactive processes between two or more learning partners. Collaboration scripts consist of at least five components: (a) learning objectives, (b) type of activities, (c) sequencing, (d) role distribution, and (e) type of representation. These components serve as a basis for comparing prototypical collaboration script approaches for face-to-face vs. computer-mediated learning. As our analysis reveals, collaboration scripts for face-to-face learning often focus on supporting collaborators in engaging in activities that are specifically related to individual knowledge acquisition. Scripts for computer-mediated collaboration are typically concerned with facilitating communicative-coordinative processes that occur among group members. The two lines of research can be consolidated to facilitate the design of collaboration scripts, which both support participation and coordination, as well as induce learning activities closely related to individual knowledge acquisition and metacognition. In addition, research on collaboration scripts needs to consider the learnersโ internal collaboration scripts as a further determinant of collaboration behavior. The article closes with the presentation of a conceptual framework incorporating both external and internal collaboration scripts
Power to the Teachers:An Exploratory Review on Artificial Intelligence in Education
This exploratory review attempted to gather evidence from the literature by shedding light on the emerging phenomenon of conceptualising the impact of artificial intelligence in education. The review utilised the PRISMA framework to review the analysis and synthesis process encompassing the search, screening, coding, and data analysis strategy of 141 items included in the corpus. Key findings extracted from the review incorporate a taxonomy of artificial intelligence applications with associated teaching and learning practice and a framework for helping teachers to develop and self-reflect on the skills and capabilities envisioned for employing artificial intelligence in education. Implications for ethical use and a set of propositions for enacting teaching and learning using artificial intelligence are demarcated. The findings of this review contribute to developing a better understanding of how artificial intelligence may enhance teachers’ roles as catalysts in designing, visualising, and orchestrating AI-enabled teaching and learning, and this will, in turn, help to proliferate AI-systems that render computational representations based on meaningful data-driven inferences of the pedagogy, domain, and learner models
Context based learning: a survey of contextual indicators for personalized and adaptive learning recommendations. A pedagogical and technical perspective
Learning personalization has proven its effectiveness in enhancing learner
performance. Therefore, modern digital learning platforms have been
increasingly depending on recommendation systems to offer learners personalized
suggestions of learning materials. Learners can utilize those recommendations
to acquire certain skills for the labor market or for their formal education.
Personalization can be based on several factors, such as personal preference,
social connections or learning context. In an educational environment, the
learning context plays an important role in generating sound recommendations,
which not only fulfill the preferences of the learner, but also correspond to
the pedagogical goals of the learning process. This is because a learning
context describes the actual situation of the learner at the moment of
requesting a learning recommendation. It provides information about the learner
current state of knowledge, goal orientation, motivation, needs, available
time, and other factors that reflect their status and may influence how
learning recommendations are perceived and utilized. Context aware recommender
systems have the potential to reflect the logic that a learning expert may
follow in recommending materials to students with respect to their status and
needs. In this paper, we review the state-of-the-art approaches for defining a
user learning-context. We provide an overview of the definitions available, as
well as the different factors that are considered when defining a context.
Moreover, we further investigate the links between those factors and their
pedagogical foundations in learning theories. We aim to provide a comprehensive
understanding of contextualized learning from both pedagogical and technical
points of view. By combining those two viewpoints, we aim to bridge a gap
between both domains, in terms of contextualizing learning recommendations
Computational and Robotic Models of Early Language Development: A Review
We review computational and robotics models of early language learning and
development. We first explain why and how these models are used to understand
better how children learn language. We argue that they provide concrete
theories of language learning as a complex dynamic system, complementing
traditional methods in psychology and linguistics. We review different modeling
formalisms, grounded in techniques from machine learning and artificial
intelligence such as Bayesian and neural network approaches. We then discuss
their role in understanding several key mechanisms of language development:
cross-situational statistical learning, embodiment, situated social
interaction, intrinsically motivated learning, and cultural evolution. We
conclude by discussing future challenges for research, including modeling of
large-scale empirical data about language acquisition in real-world
environments.
Keywords: Early language learning, Computational and robotic models, machine
learning, development, embodiment, social interaction, intrinsic motivation,
self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J.
Horst and J. von Koss Torkildsen, Routledg
Designing for change: mash-up personal learning environments
Institutions for formal education and most work places are equipped today with at least some kind of tools that bring together people and content artefacts in learning activities to support them in constructing and processing information and knowledge. For almost half a century, science and practice have been discussing models on how to bring personalisation through digital means to these environments. Learning environments and their construction as well as maintenance makes up the most crucial part of the learning process and the desired learning outcomes and theories should take this into account. Instruction itself as the predominant paradigm has to step down.
The learning environment is an (if not 'theรฏยฟยฝ) important outcome of a learning process, not just a stage to perform a 'learning play'. For these good reasons, we therefore consider instructional design theories to be flawed.
In this article we first clarify key concepts and assumptions for personalised learning environments. Afterwards, we summarise our critique on the contemporary models for personalised adaptive learning. Subsequently, we propose our alternative, i.e. the concept of a mash-up personal learning environment that provides adaptation mechanisms for learning environment construction and maintenance. The web application mash-up solution allows learners to reuse existing (web-based) tools plus services.
Our alternative, LISL is a design language model for creating, managing, maintaining, and learning about learning environment design; it is complemented by a proof of concept, the MUPPLE platform. We demonstrate this approach with a prototypical implementation and a โ we think โ comprehensible example. Finally, we round up the article with a discussion on possible extensions of this new model and open problems
์ธ๊ณต์ง๋ฅ ๊ธฐ๋ฐ ๊ต์ก ํ๋ซํผ ์ฌ์ฉ์ ๋ํ ์ค๊ตญ ๊ต์ฌ์ ์ธ์
ํ์๋
ผ๋ฌธ (์์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ์ฌ๋ฒ๋ํ ๊ต์กํ๊ณผ, 2021. 2. ์กฐ์ํ.์ต๊ทผ ๊ต์ก ๋ถ์ผ์์ ์ธ๊ณต์ง๋ฅ(AI)์ ๋์
์ด ํฐ ๊ด์ฌ์ ๋๊ณ ์๋ค. ํนํ AI ๊ธฐ์ ๊ณผ ํ์ต ๋ถ์์ด ๊ฒฐํฉํ ์ธ๊ณต์ง๋ฅ ๊ธฐ๋ฐ ๊ต์ก ํ๋ซํผ์ ์ง๊ธ๊ป ์คํ๋๊ธฐ ์ด๋ ค์ ๋ ๋ง์ถคํ ํ์ต(personalized learning)๊ณผ ์ ์์ ํ์ต(adaptive learning)์ ๋์์ด ๋ ์ ์๋๋ก ๋ฐ์ ํ๊ณ ์๋ค. ์ธ๊ณต์ง๋ฅ ๊ธฐ๋ฐ ๊ต์ก ํ๋ซํผ(AI-based education platform)์ ํ์ต์์ ํ๋ ์ถ์ ๋ฑ์ ํตํด ์ด๋ค์ ํน์ฑ์ ๋ถ์ํ๊ณ ์ง๋จ์ ์ ๊ณตํ ๋ค ๋ถ์ ๊ฒฐ๊ณผ๋ฅผ ํ ๋๋ก ํ์ต์์๊ฒ ์ธ์ง ์์ค์ ๋ง๋ ๋ง์ถคํ ํ์ต์์๊ณผ ํผ๋๋ฐฑ์ ์ ๊ณตํ๋ค. ์ธ๊ณต์ง๋ฅ ๊ธฐ๋ฐ ๊ต์ก ํ๋ซํผ์ ๊ต์ฌ์ ํ์์๊ฒ ์ค์๊ฐ ํ์ต ๋ฐ์ดํฐ์ ๋ถ์ ๊ฒฐ๊ณผ, ๊ทธ๋ฆฌ๊ณ ํผ๋๋ฐฑ์ ์ ๊ณตํ ์ ์์ด ์ธ๊ณต์ง๋ฅ ๊ธฐ๋ฐ ๊ต์ก ํ๋ซํผ์ด ๋ง์ถคํ ํ์ต์ ๊ธ์ ์ ์ธ ์๋ฏธ๊ฐ ์๋ค๋ ์ ํ ์ฐ๊ตฌ๋ ์์๋ค. ๊ทธ๋ผ์๋ ๋ถ๊ตฌํ๊ณ , ๊ธฐ์กด ์ฐ๊ตฌ๋ ๋ชจ๋ธ ๊ฐ๋ฐ์ ์ฐจ์์์๋ ์๋ฐํ ์คํ์ค ํ๊ฒฝ์์ ์ธ๊ณต์ง๋ฅ ๊ธฐ๋ฐ ๊ต์ก ํ๋ซํผ์ ํจ๊ณผ๋ฅผ ์ฐ๊ตฌํด์์ผ๋ฉฐ, ์ธ๊ณต์ง๋ฅ ๊ธฐ๋ฐ ๊ต์ก ํ๋ซํผ์ ๋ํ ๊ต์ฌ์ ์ธ์๊ณผ ๊ด๋ จ๋ ์ฐ๊ตฌ๋ ๋๋ฌผ์๋ค. ๊ต์ฌ๋ ์ธ๊ณต์ง๋ฅ ๊ต์ก ๊ธฐ์ ์ ์ฌ์ฉ์์ด๊ธฐ ๋๋ฌธ์ ์ธ๊ณต์ง๋ฅ ๊ต์ก ๊ธฐ์ ์ ๊ต์ก ๋์
์ ์์ด ๊ต์ฌ๋ค์ ์ธ์๊ณผ ์๊ฒฌ์ ์ค์ํ๋ค.
๋ณธ ์ฐ๊ตฌ๋ ์ธ๊ณต์ง๋ฅ ๊ธฐ๋ฐ ๊ต์ก ํ๋ซํผ์ ํ์ฉํ๋ ๊ฒ์ ๋ํ ๊ต์ฌ๋ค์ ์ธ์์ ํ๊ตฌํ์๋ค. ์๋ ์ฐ๊ตฌ ๋ฌธ์ ๋ฅผ ๋ค๋ฃจ๊ธฐ ์ํด ์ง์ ์ฐ๊ตฌ๋ฅผ ์ํํ์๋ค. ์ฒซ์งธ, ์ค๊ตญ ๊ต์ฌ๋ค์ ์ธ๊ณต์ง๋ฅ ๊ธฐ๋ฐ ๊ต์ก ํ๋ซํผ์ด ์คํ๊ต ๊ต์ก์ ํ์ฉ ์์ด ์ด๋ ํ ์ฅ์ ์ด ์๋ค๊ณ ์ธ์ํ๋๊ฐ? ๋์งธ, ์ค๊ตญ ๊ต์ฌ๋ค์ ์ธ๊ณต์ง๋ฅ ๊ธฐ๋ฐ ๊ต์ก ํ๋ซํผ๊ณผ ์คํ๊ต ๊ต์ ํ๋ ์์ ๊ฐ ์ด๋ ํ ๋ชจ์์ด ์๋ค๊ณ ์ธ์ํ๋๊ฐ? ์
์งธ, ์ค๊ตญ ๊ต์ฌ๋ค์ ์ธ๊ณต์ง๋ฅ ๊ธฐ๋ฐ ๊ต์ก ํ๋ซํผ์ ์คํ๊ต ๊ต์ก์ ๋์
ํ ๋ ๋ฌด์์ด ํ์ํ๋ค๊ณ ์ธ์ํ๋๊ฐ? ๋ณธ ์ฐ๊ตฌ๋ ์ค๊ตญ ๊ต์ฌ๋ค์ ์ฐ๊ตฌ๋์์ผ๋ก ์จ๋ผ์ธ ์ฌ์ธต ๋ฉด๋ด์ ํ์๋ค. ๋ฌธํ ๋ฆฌ๋ทฐ๋ฅผ ํตํด ๋ฉด๋ด ์ง๋ฌธ์ง๋ฅผ ์ค๊ณํ๋ ๋๋ฉ์ดํ์ง๋ฒ (snowball sampling)์ ํตํด ์ค๊ตญ ์คํ๊ต ๊ต์ฌ 14๋ช
์ ์ฐ๊ตฌ์ฐธ์ฌ์๋ก ์ ์ ํ์๋ค. ์ ์ ๋ ๊ต์ฌ๋ค์ ๋ชจ๋ ์ธ๊ณต์ง๋ฅ ๊ธฐ๋ฐ ๊ต์ก ํ๋ซํผ ์ฌ์ฉ ๊ฒฝํ์ด ์์ผ๋ฉฐ ๊ฐ ๊ต์ฌ๋ฅผ ๋์์ผ๋ก ์ฝ 1์๊ฐ ์ ๋ ๋ฉด๋ด์ ์งํํ๊ณ ๋
น์ํ์๋ค. ๋ฉด๋ด์ด ๋๋ ํ ๋
น์ ๋ด์ฉ์ ์ ์ฌํ์์ผ๋ฉฐ, ์ฃผ์ ๋ถ์์ ์ฌ์ฉํ์ฌ ๋ฉด๋ด ๋ด์ฉ์ ์ด๊ธฐ ์ฝ๋ ์์ฑํ๊ณ ๋ฉด๋ด ์๋ฃ ์์์ ์ฃผ์ ๋ฅผ ๋์ถํ์๋ค. ํนํ ์ฐ๊ตฌ ๋ฌธ์ 2๋ฒ์ ๊ฒฝ์ฐ, ์ธ๊ณต์ง๋ฅ ๊ธฐ๋ฐ ๊ต์ก ํ๋ซํผ ํ์ฉ๊ณผ ๊ต์ ํ์ตํ๋ ๋ด ์ฌ๋ฌ ์์ ๊ฐ์ ๋ชจ์์ ๋ถ์ํ๊ธฐ ์ํด ํ๋์ด๋ก ์ ์ฐ๊ตฌ์ ํ๋ก ์ด์ฉํ์๋ค. ์ต์ข
์ ์ผ๋ก ์ฐ๊ตฌ๋ฌธ์ 1์ ๋ํ ์ฃผ์ 4๊ฐ, ์ฐ๊ตฌ๋ฌธ์ 2์ ๋ํ ์ฃผ์ 6๊ฐ, ์ฐ๊ตฌ๋ฌธ์ 3์ ๋ํ ์ฃผ์ 4๊ฐ๋ฅผ ๋์ถํ์๋ค.
์ฐ๊ตฌ ๊ฒฐ๊ณผ๋ก ๊ต์ฌ๋ค์ ์ธ๊ณต์ง๋ฅ ๊ธฐ๋ฐ ๊ต์ก ํ๋ซํผ์ ์ฅ์ ์ ๋ํด ์ฆ๊ฐ์ ์ธ ํผ๋๋ฐฑ ์ ๊ณต, ๊ต์ํ์ต ์ง์, ๊ต์ฌ์ ์
๋ฌด๋ ๊ฐ์ ๋ฑ์ผ๋ก ์ธ์ํ์๊ณ , ์ธ๊ณต์ง๋ฅ ๊ธฐ๋ฐ ๊ต์ก ํ๋ซํผ์ด ๋ค์ํ ๊ต์ํ์ต ์์์ ํตํฉํ ์ ์๋ค๊ณ ์ธ์ํ์๋ค. ์์ธ๋ฌ ๊ต์ฌ๋ค์ ์ธ๊ณต์ง๋ฅ ๊ธฐ๋ฐ ๊ต์ก ํ๋ซํผ์ ์ฌ์ฉ์ ์์ด ๊ธฐ์กด์ ๊ต์ํ์ต ํ๋๊ณผ ์์ถฉ๋ ๋ถ๋ถ์ด ์๋ค๋ ์ ์ ์ธ์ํ์๋ค. ๊ต์ฌ๋ค์ ๊ธฐ์กด ์ธ๊ณต์ง๋ฅ ๊ธฐ๋ฐ ๊ต์ก ํ๋ซํผ์ ์ถ์ฒ ๋ชจ๋ธ์ด ์ฐจ๋ณํ๋ ํ์๋ค์๊ฒ ์ ์ ์ฉ๋์ง ๋ชปํ๋ค๋ ๊ฒ์ ์ธ์ํ์๋ค. ๊ทธ๋ฆฌ๊ณ ๊ธฐ์กด ์ธ๊ณต์ง๋ฅ ๊ธฐ๋ฐ ๊ต์ก ํ๋ซํผ์ด ๋ค์ํ ํ์ต ์์์ ์ ๋ถ๋ฅ๋์ง ๋ชปํ๊ธฐ ๋๋ฌธ์ ๊ต์ฌ๋ค์ด ์ฌ์ฉํ๊ธฐ ๋ถํธํ๋ค. ์ธ๊ณต์ง๋ฅ ๊ธฐ๋ฐ ๊ต์ก ํ๋ซํผ์ ์ด์ฉํ ๋ ๊ต์ฌ์ ์ง์ ์ฌ์ฐ๊ถ์ ๋ณดํธํ๊ธฐ ์ํ ๋ช
ํํ ๊ท์ ๊ฐ ๋ถ์กฑํ๋ค๊ณ ์ธ์ํ์๋ค. ์ด์ ํจ๊ป ํ๋ถ๋ชจ๋ค์ ์ธ๊ณต์ง๋ฅ ๊ธฐ๋ฐ ๊ต์ก ํ๋ซํผ์ ์ฌ์ฉํจ์ผ๋ก์จ ๋ฐ์ํ ์ ์๋ ํ์ต์์ ์ธํฐ๋ท ๋จ์ฉ๊ณผ ์๋ ฅ ์ ํ ๋ฌธ์ ๋ฅผ ์ฐ๋ คํ์๋ค. ๋ ์ค๊ตญ์ ์ฌํ๋ฌธํ์ ๋ฐฐ๊ฒฝ๊ณผ ๊ต์ก ํน์ฑ์ผ๋ก ์ธํด ์ธ๊ณต์ง๋ฅ ๊ธฐ๋ฐ ๊ต์ก ํ๋ซํผ์ ํ์ฉํ๋ ๋ฐ ํ์๋ค์ ๊ธ์จ ์ฐ๊ธฐ ๋ฅ๋ ฅ์ ์ํฅ์ ๋ฏธ์น ์ ์์ผ๋ฉฐ, ํ๊ต ๋ด ์ ์๊ธฐ๊ธฐ ์ฌ์ฉ ์ ํ๋ ๋ฐ์ดํฐ ์์ง์ ์ง์์ฑ๊ณผ ํจ์จ์ฑ์ ์ํฅ์ ๋ฏธ์น ์ ์๋ค๊ณ ์ธ์ํ์๋ค. ๊ต์ฌ๋ค์ ์์ ๋ฌธ์ ๋ค์ด ์ธ๊ณต์ง๋ฅ ๊ต์ก ํ๋ซํผ ์ฌ์ฉ์ ๋ํ ๊ท์น ๋ง๋ จ๊ณผ ์ธ๊ณต์ง๋ฅ ๊ธฐ์ ์ ๊ฐ์ ํจ์ผ๋ก์จ ์ํ๋ ์ ์๋ค๊ณ ์ธ์ํ์๋ค. ๋ํ ๊ต์ฌ์ ์ค์ ์๊ตฌ์ ๋ง๊ฒ ๊ฐ๋ฐ๋ ์ ์๋๋ก ์ธ๊ณต์ง๋ฅ ๊ธฐ๋ฐ ๊ต์ก ํ๋ซํผ ๊ฐ๋ฐ ๊ณผ์ ์ ๊ต์ก ์ ๋ฌธ๊ฐ์ ๊ต์ฌ๊ฐ ์ฐธ์ฌํ ํ์๊ฐ ์๋ค.
๋ณธ ์ฐ๊ตฌ๋ ์ค๊ตญ ๊ต์ฌ๋ค์ด ์ธ๊ณต์ง๋ฅ ๊ธฐ๋ฐ ๊ต์ก ํ๋ซํผ์ ๋ํ ์ธ์์ ํ์ํ์์ผ๋ฉฐ, ์ธ๊ณต์ง๋ฅ ๊ธฐ๋ฐ ๊ต์ก ํ๋ซํผ์ด ๊ต์ํ์ต์์์ ์ฅ์ ๊ณผ ๋ฌธ์ ์ ์ ๋ฐํ๋ค. ์์ธ๋ฌ ๋ณธ ์ฐ๊ตฌ๋ ์ธ๊ณต์ง๋ฅ ๊ธฐ๋ฐ ๊ต์ก ํ๋ซํผ์ด ๊ต์ก ๋ถ์ผ์ ๋๊ท๋ชจ๋ก ๋์
๋ ์ ์๋๋ก ๊ท์น, ์ธ๊ณต์ง๋ฅ ๊ธฐ์ , ๊ทธ๋ฆฌ๊ณ ๊ต์ก ๊ณตํ์ ์ฐจ์์์ ์ฌ์ฉ ๊ท๋ฒ๊ณผ ๊ธฐ์ ๊ฐ์ ์ ์ ์ํ์๋ค. ๋ณธ ์ฐ๊ตฌ๋ฅผ ํตํด ํ์ํ ๋ด์ฉ์ด ํฅํ ๊ต์ก ๋ถ์ผ์ ์ธ๊ณต์ง๋ฅ ๊ธฐ๋ฐ ๊ต์ก ํ๋ซํผ ๋์
์ ํ์ฉ๋๋ค๋ฉด ์ธ๊ณต์ง๋ฅ ๊ต์ก ๊ธฐ์ ์ ๊ดํ ์ฐ๊ตฌ์ ๋ฐ์ ์๋ ๊ธฐ์ฌํ ์ ์์ ๊ฒ์ผ๋ก ๊ธฐ๋๋๋ค.In recent years, the introduction of artificial intelligence (AI) in education has attracted widespread attention. In particular, the AI-based education platform based on the combination of AI technology and learning analysis brings new light to the long-standing difficulties in personalized learning and adaptive learning. The AI-based education platform analyzes learners' characteristics by collecting their data and tracking their learning behavior. It then generates cognitive diagnosis for learners and provides them with personalized learning resources and adaptive feedback that match their cognitive level based on systematic analysis. With the help of the AI-based education platform, teachers and students can get real-time educational data and analysis result๏ผas well as the feedback and treatment corresponding to the results. Previous studies have already demonstrated and proved its positive significance to personalized learning. However, these studies mostly start from a model development perspective or in a rigorous laboratory environment. There has been little research on teachers' perceptions of AI-based education platform. As a direct user of AI educational technologies, teachers' perceptions and suggestions are vital for introducing AIEd in education.
In this study, the researcher explored teachers' perceptions of using AI-based education platform in teaching.
The study conducted qualitative research to address the following research questions: 1) How do Chinese teachers perceive the advantages of AI-based education platforms for teaching and learning in secondary school? 2) How do Chinese teachers perceive the contradictions between AI-based education platforms and the secondary school system? 3๏ผHow do Chinese teachers suggest applying AI-based education platforms in secondary school? And it referred to the in-depth online interview with Chinese teachers who had experience with AI-based education platform.
Interview questions were constructed through the literature review, and 14 secondary school teachers were selected by the snowball sampling method. The interviews lasted for an average of one hour per teacher and were transcribed from the audio recordings to text documents when finished. Afterward, the data were analyzed using thematic analysis, including generating initial codes, searching and reviewing the categories, and deriving the themes finally. Notably, for research question two, the researcher used the activity theory framework to analyze the contradictions among the use of the AI-based education platform and the various elements of the teaching and learning activities. Finally, four themes for research question 1, six themes for research question 2, and four themes for research question 3 were derived.
As for the advantages, teachers believe that AI-based education platforms can provide instant feedback, targeted and systematic teaching support, and reduce teachers' workload. At the same time, AI-based education platforms can also integrate teaching resources in different areas. Teachers also recognized that the AI-based education platforms might trigger contradictions in existing teaching activities. They are aware of the situation that the recommended model of the AI-based education platform is not suitable for all levels of students; that a large number of learning resources are not classified properly enough to meet the needs of teachers, and that there lack clear rules and regulations to protect teachers' intellectual property rights when using the platform. Besides, parents are also concerned about the potential risk of internet addiction and vision problems using AI-based education platforms. Moreover, the use of the AI-based education platform may also affect students' ability to write Chinese characters due to the socio-historical background and educational characteristics in China. Furthermore, the restricted use of electronic devices on campus may also impact the consistent and effective education data collection. Teachers believe that these problems can be solved by improving rules and AI technology. Moreover, to make the platform more in line with the actual teaching requirements, teachers and education experts can also be involved in the development process of AI-based education platform.
This study explored how Chinese teachers perceive the AI-based education platform and found that the AI-based education platform was conducive to personalized teaching and learning. At the same time, this study put forward some suggestions from the perspective of rules, AI technology, and educational technology, hoping to provide a good value for the future large-scale introduction of AI-based education platforms in education.CHAPTER 1. INTRODUCTION 1
1.1. Problem Statement 1
1.2. Purpose of Research 7
1.3. Definition of Terms 8
CHAPTER 2. LITERATURE REVIEW 10
2.1. AI in Education 10
2.1.1 AI for Learning and Teaching 10
2.1.2 AI-based Education Platform 14
2.1.3 Teachers' Perception on AI-based Education Platform 18
2.2. Activity Theory 20
CHAPTER 3. RESEARCH METHOD 23
3.1. Research Design 23
3.2. Participants 25
3.3. Instrumentation 26
3.3.1 Potential Value of AI System in Education 26
3.4. Data Collection 33
3.5. Data Analysis 34
CHAPTER 4. FINDINGS 36
4.1. Advantages of Using AI-based Education Platform 36
4.1.1 Instant Feedback 37
4.1.2 Targeted and Systematic Teaching Support 42
4.1.3 Educational Resources Sharing 46
4.1.4 Reducing Workload 49
4.2. Tensions of Using AI-based Education Platform 51
4.2.1 Inadequately Meet the Needs of Teachers 52
4.2.2 Failure to Satisfy Low and High Achievers 54
4.2.3 Intellectual Property Violation 56
4.2.4 Guardian's Concern 57
4.2.5 School Rules about the Use of Electronic Devices 58
4.2.6 Implication for Chinese Character Education 59
4.3. Suggestion of Using AI-based Education Platform 61
4.3.1 Improving Rules of Using the AI-based Education Platform 61
4.3.2 Improving Rules of Protecting Teachers Right 62
4.3.3 Improving AI Technology 64
4.3.4 Participatory Design 66
CHAPTER 5. DISCUSSION AND CONCLUSION 68
5.1. Discussion 68
5.2. Conclusion 72
REFERENCE 75
APPENDIX 1 98
APPENDIX 2 100
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