Query: Learners artificial intelligence literacy

title: Artificial Intelligence Literacy based Teaching Conditions and Model for Improving Primary English Language Learners' Global Digital Citizenship

title: Artificial Intelligence Literacy's Big Ideas and Achievement Standards for Primary English Language Learners

title: Digital Content to Improve Artificial Intelligence Literacy Ability
abstract: This study aims to design and develop effective digital contents to improve the ability for artificial intelligence literacy. First, we defined AI literacy and analyzed the competencies required for artificial intelligence literacy. After selecting the educational elements for AI ability, we composed 10 educational programs. To confirm the appropriateness of designed contents, we verified through content validity test by 10 experts. The CVI value was over 0.75, which was highly valid. The developed content was installed on the online system and applied to 55 AI beginners for 4 weeks. The learners showed a positive result of at least 3.85 in the items of content difficulty, understanding, effectiveness, and learning challenge. As a result of this analysis, we can see that the developed content is positive for helping many people understand AI and improving AI literacy.

title: Assessment of Optimal Pedagogical Factors for Canadian ESL Learners' Reading Literacy Through Artificial Intelligence Algorithms
abstract: The current study explored the effective pedagogical factors that distinguish high achieving from low achieving ESL (English as a second language) primary school learners in reading literacy in Canada. In total, 203 samples (167 high achieving learners and 36 low achieving learners from 128 primary schools) in the 4th grade were drawn from the public database of Progress in International Reading Literacy Study (PIRLS) 2016, which is the benchmark for large scale assessments of reading literacy targeting fourth grade students. For the first time in the ESL related research, this study applied an artificial intelligence approach, support vector machine (SVM) to concurrently analyze 41 pedagogical factors associated with reading materials, classroom organization, reading strategies, in class reading activities and post reading activities. The overall 41 factors collectively distinguished the high achieving readers from the low achieving readers with a high accuracy score (0.793) via SVM. Then, these 41 factors were ranked according to their contribution to the SVM model through SVM based recursive feature elimination (SVM RFE) Eventually, an optimal factor set was selected by the SVM RFE cross validation, which contained 10 effective pedagogical factors centered on reading materials, reading strategies and in class reading activities for fourth grade high achieving ESL learners in reading literacy. Suggestions based on solid data analysis would facilitate infrastructural and pedagogical improvements in ESL reading education.

title: Educational gamification and artificial intelligence for promoting digital literacy
abstract: There is a need for promoting digital literacy and providing education for all especially women of the world to achieve the United Nations Sustainable Development Groups (UNSDGs) 4 and 5 quality education and gender equality. The field of education is continuously exploring the methods to respond to the fast approaching impact of artificial intelligence across all sectors and fields including its own. Right from e commerce to health care to education, in every sector, the intervention of artificial intelligence has increased multifold. Especially in education, it is more as artificial intelligence systems can adapt to individual student learning and grasping abilities to give hyper personalized learning experience. In addition to AI, involving gamification would be an add on that would lead to better user engagement and encourages them to be continuous learners, it also converts the conventional classroom experience into a competitive multi player gaming and learning platform, and efforts can be put to promote digital literacy among them while both AI and gamification is being implemented.

title: Design and Development of High School Artificial Intelligence Textbook Based on Computational Thinking
abstract: Big data and deep learning technology have once again set off a boom in artificial intelligence. Artificial intelligence has been a major development strategy in many countries. The Chinese government has also written this into the "13th Five Year Plan" and the Ministry of Education has also launched with the reform measures for artificial intelligence education, the deep integration of "artificial intelligence education" has been opened. In this context, China's artificial intelligence disciplines and professional construction have set off a major reform storm from colleges to basic education. The revision of this high school artificial intelligence textbook was carried out in this context. The biggest feature of this high school artificial intelligence textbook revision is the integration of computational thinking into the core literacy design. Computational thinking is an indispensable ability of learners in the 21st century. As a typical "digital aboriginal" high school students must have computational thinking in order to cope with the challenges brought about by the rapid changes in social technology in the future. At present, all developed countries have incorporated computational thinking into national curriculum standards. China has also written computational thinking into national curriculum standards and listed it as one of the core literacies of information technology disciplines.

title: Collaborating with Mature English Language Learners to Combine Peer and Automated Feedback: a User Centered Approach to Designing Writing Support
abstract: 300,000 immigrants move to Canada each year in search of better economic opportunities, and many have limited English language skills. Improving written literacy of newcomers can enhance education, employment, or social integration opportunities. However, frequent, timely, and personalized feedback is not always possible for immigrants. Online writing support tools can scaffold writing development by providing this feedback, but existing systems provide inadequate support when instructors are inaccessible. In this paper, we show how feedback system design can leverage peer and automated feedback to support mature English Language Learners' (ELL) needs and practices. We identify strong associations between epistemic beliefs and learning strategies, highlighting the importance of tasks that activate productive epistemic beliefs. We find learners accurately assessed high level issues in a peer's writing and are accepting of automated feedback, demonstrating that a platform combining peer review and machine feedback could promote meaningful discussions. We present the results of our mixed methods investigation that integrates three sources of information: analysis of learners' psychometric constructs, writing samples to identify error patterns, and participatory design group sessions incorporating human centred design methods. We synthesize our results into four guidelines derived from seven findings resulting from the investigation of a system that scaffolds writing development for mature immigrant ELLs in the absence of formal instructional support. First, we find that ELLs require a platform to collaboratively iterate through the writing process. Next, we suggest how peer feedback can be enhanced through automated support. We then demonstrate how rubric design can guide both linear and holistic peer review. Finally, we illustrate why open learner models and learning dashboards should contextualize real world progress.

title: Automated Disengagement Tracking Within an Intelligent Tutoring System
abstract: This paper describes a new automated disengagement tracking system (DTS) that detects learners' maladaptive behaviors, e.g. mind wandering and impetuous responding, in an intelligent tutoring system (ITS) called AutoTutor. AutoTutor is a conversation based intelligent tutoring system designed to help adult literacy learners improve their reading comprehension skills. Learners interact with two computer agents in natural language in 30 lessons focusing on word knowledge, sentence processing, text comprehension, and digital literacy. Each lesson has one to three dozen questions to assess and enhance learning. DTS automatically retrieves and aggregates a learner's response accuracies and time on the first three to five questions in a lesson, as a baseline performance for the lesson when they are presumably engaged, and then detects disengagement by observing if the learner's following performance significantly deviates from the baseline. DTS is computed with an unsupervised learning method and thus does not rely on any self reports of disengagement. We analyzed the response time and accuracy of 252 adult literacy learners who completed lessons in AutoTutor. Our results show that items that the detector identified as the learner being disengaged had a performance accuracy of 18.5% in contrast to 71.8% for engaged items. Moreover, the three post test reading comprehension scores from Woodcock Johnson III, RISE, and RAPID had a significant association with the accuracy of engaged items, but not disengaged items.

title: AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring
abstract: AutoTutor is a natural language tutoring system that has produced learning gains across multiple domains (e.g. computer literacy, physics, critical thinking) In this paper, we review the development, key research findings, and systems that have evolved from AutoTutor. First, the rationale for developing AutoTutor is outlined and the advantages of natural language tutoring are presented. Next, we review three central themes in AutoTutor's development: human inspired tutoring strategies, pedagogical agents, and technologies that support natural language tutoring. Research on early versions of AutoTutor documented the impact on deep learning by co constructed explanations, feedback, conversational scaffolding, and subject matter content. Systems that evolved from AutoTutor added additional components that have been evaluated with respect to learning and motivation. The latter findings include the effectiveness of deep reasoning questions for tutoring multiple domains, of adapting to the affect of low knowledge learners, of content over surface features such as voices and persona of animated agents, and of alternative tutoring strategies such as collaborative lecturing and vicarious tutoring demonstrations. The paper also considers advances in pedagogical agent roles (such as trialogs) and in tutoring technologies, such semantic processing and tutoring delivery platforms. This paper summarizes and integrates significant findings produced by studies using AutoTutor and related systems.

title: Digital Competence in the Knowledge Society
abstract: New Information and Communication Technologies such as the Internet, online gaming worlds, artificial intelligence, robotics and 3D printing require new literacies. In recent years, digital competence has become a key concept in discussions on the kind of skills and understanding learners need in the Knowledge Society. The concept has been interpreted in various ways (e.g. Digital Literacy, Digital Competence, eLiteracy, e Skills, eCompetence, Computer literacy, and Media literacy) in policy documents, in the academic literature, and in teaching, learning and certification practices. In this paper we review the literature on digital competence and related terms. This review of 73 articles published between 1990 and 2014 shows that digital competence is a multi faceted concept that has emerged from several backgrounds. Not yet a stable concept, there are still no clear guidelines for evaluating it. While some perceive digital competence as the technical use of ICT, others define it more broadly as knowledge application or 21st century skills.