Opening Up an Intelligent Tutoring System Development Environment for Extensible Student Modeling

ITS authoring tools make creating intelligent tutoring systems more cost effective, but few authoring tools make it easy to flexibly incorporate an open-ended range of student modeling methods and learning analytics tools. To support a cumulative science of student modeling and enhance the impact of real-world tutoring systems, it is critical to extend ITS authoring tools so they easily accommodate novel student modeling methods. We report on extensions to the CTAT/Tutorshop architecture to support a plug-in approach to extensible student modeling, which gives an author full control over the content of the student model. The extensions enhance the range of adaptive tutoring behaviors that can be authored and support building external, student- or teacher-facing real-time analytics tools. The contributions of this work are: (1) an open architecture to support the plugging in, sharing, re-mixing, and use of advanced student modeling techniques, ITSs, and dashboards; and (2) case studies illustrating diverse ways authors have used the architecture

Challenges and opportunities in the use of artificial intelligence in education for academic writing: A scoping review

Artificial intelligence in education emerged as a highly significant and transformational technological advancement. Academic writing has witnessed significant advancements and widespread adoption of artificial intelligence (AI) tools and approaches across several application domains. AI-based tools for scientific writing are beneficial but also have some challenges. This scoping study aims to comprehensively examine the opportunities and challenges of AI in Education (AIEd) for Academic Writing by analyzing literature published over the last 8 years (2015–2023) from diverse national journal sites. The primary findings can be divided into two categories. First, the benefits of implementing AI in education for academic writing include improved writing skills and personalized feedback, writing assignment management, multilingual and multicultural support, collaboration and interactive support, and ease of access and inclusiveness. Second, the challenges in implementing AI in Education for academic writing include ethics and academic integrity, development of technical AIed skills, bias, personalized learning, roles and changes in education, evaluation and validity, and instructor understanding and utilization of AIed

Challenges and opportunities in the use of artificial intelligence in education for academic writing: A scoping review

Artificial intelligence in education emerged as a highly significant and transformational technological advancement. Academic writing has witnessed significant advancements and widespread adoption of artificial intelligence (AI) tools and approaches across several application domains. AI-based tools for scientific writing are beneficial but also have some challenges. This scoping study aims to comprehensively examine the opportunities and challenges of AI in Education (AIEd) for Academic Writing by analyzing literature published over the last 8 years (2015–2023) from diverse national journal sites. The primary findings can be divided into two categories. First, the benefits of implementing AI in education for academic writing include improved writing skills and personalized feedback, writing assignment management, multilingual and multicultural support, collaboration and interactive support, and ease of access and inclusiveness. Second, the challenges in implementing AI in Education for academic writing include ethics and academic integrity, development of technical AIed skills, bias, personalized learning, roles and changes in education, evaluation and validity, and instructor understanding and utilization of AIed