Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education

This paper presents a novel framework, Artificial Intelligence-Enabled Intelligent Assistant (AIIA), for personalized and adaptive learning in higher education. The AIIA system leverages advanced AI and Natural Language Processing (NLP) techniques to create an interactive and engaging learning platform. This platform is engineered to reduce cognitive load on learners by providing easy access to information, facilitating knowledge assessment, and delivering personalized learning support tailored to individual needs and learning styles. The AIIA's capabilities include understanding and responding to student inquiries, generating quizzes and flashcards, and offering personalized learning pathways. The research findings have the potential to significantly impact the design, implementation, and evaluation of AI-enabled Virtual Teaching Assistants (VTAs) in higher education, informing the development of innovative educational tools that can enhance student learning outcomes, engagement, and satisfaction. The paper presents the methodology, system architecture, intelligent services, and integration with Learning Management Systems (LMSs) while discussing the challenges, limitations, and future directions for the development of AI-enabled intelligent assistants in education.Comment: 29 pages, 10 figures, 9659 word

Utility Analysis for Optimizing Compact Adaptive Spectral Imaging Systems for Subpixel Target Detection Applications

Since the development of spectral imaging systems where we transitioned from panchromatic, single band images to multiple bands, we have pursued a way to evaluate the quality of spectral images. As spectral imaging capabilities improved and the bands collected wavelengths outside of the visible spectrum they could be used to gain information about the earth such as material identification that would have been a challenge with panchromatic images. We now have imaging systems capable of collecting images with hundreds of contiguous bands across the reflective portion of the electromagnetic spectrum that allows us to extract information at subpixel levels. Prediction and assessment methods for panchromatic image quality, while well-established are continuing to be improved. For spectral images however, methods for analyzing quality and what this entails have yet to form a solid framework. In this research, we built on previous work to develop a process to optimize the design of spectral imaging systems. We used methods for predicting quality of spectral images and extended the existing framework for analyzing efficacy of miniature systems. We comprehensively analyzed utility of spectral images and efficacy of compact systems for a set of application scenarios designed to test the relationships of system parameters, figures of merit, and mission requirements in the trade space for spectral images collected by a compact imaging system from design to operation. We focused on subpixel target detection to analyze spectral image quality of compact spaceborne systems with adaptive band selection capabilities. In order to adequately account for the operational aspect of exploiting adaptive band collection capabilities, we developed a method for band selection. Dimension reduction is a step often employed in processing spectral images, not only to improve computation time but to avoid errors associated with high dimensionality. An adaptive system with a tunable filter can select which bands to collect for each target so the dimension reduction happens at the collection stage instead of the processing stage. We developed the band selection method to optimize detection probability using only the target reflectance signature. This method was conceived to be simple enough to be calculated by a small on-board CPU, to be able to drive collection decisions, and reduce data processing requirements. We predicted the utility of the selected bands using this method, then validated the results using real images, and cross-validated them using simulated image associated with perfect truth data. In this way, we simultaneously validated the band selection method we developed and the combined use of the simulation and prediction tools used as part of the analytic process to optimize system design. We selected a small set of mission scenarios and demonstrated the use of this process to provide example recommendations for efficacy and utility based on the mission. The key parameters we analyzed to drive the design recommendations were target abundance, noise, number of bands, and scene complexity. We found critical points in the system design trade space, and coupled with operational requirements, formed a set of mission feasibility and system design recommendations. The selected scenarios demonstrated the relationship between the imaging system design and operational requirements based on the mission. We found key points in the spectral imaging trade space that indicated relationships within the spectral image utility trade space that can be used to further solidify the frameworks for compact spectral imaging systems

Towards a competency model for adaptive assessment to support lifelong learning

Adaptive assessment provides efficient and personalised routes to establishing the proficiencies of learners. We can envisage a future in which learners are able to maintain and expose their competency profile to multiple services, throughout their life, which will use the competency information in the model to personalise assessment. Current competency standards tend to over simplify the representation of competency and the knowledge domain. This paper presents a competency model for evaluating learned capability by considering achieved competencies to support adaptive assessment for lifelong learning. This model provides a multidimensional view of competencies and provides for interoperability between systems as the learner progresses through life. The proposed competency model is being developed and implemented in the JISC-funded Placement Learning and Assessment Toolkit (mPLAT) project at the University of Southampton. This project which takes a Service-Oriented approach will contribute to the JISC community by adding mobile assessment tools to the E-framework

Distributed Learning System Design: A New Approach and an Agenda for Future Research

This article presents a theoretical framework designed to guide distributed learning design, with the goal of enhancing the effectiveness of distributed learning systems. The authors begin with a review of the extant research on distributed learning design, and themes embedded in this literature are extracted and discussed to identify critical gaps that should be addressed by future work in this area. A conceptual framework that integrates instructional objectives, targeted competencies, instructional design considerations, and technological features is then developed to address the most pressing gaps in current research and practice. The rationale and logic underlying this framework is explicated. The framework is designed to help guide trainers and instructional designers through critical stages of the distributed learning system design process. In addition, it is intended to help researchers identify critical issues that should serve as the focus of future research efforts. Recommendations and future research directions are presented and discussed

Student-Centered Learning: Functional Requirements for Integrated Systems to Optimize Learning

The realities of the 21st-century learner require that schools and educators fundamentally change their practice. "Educators must produce college- and career-ready graduates that reflect the future these students will face. And, they must facilitate learning through means that align with the defining attributes of this generation of learners."Today, we know more than ever about how students learn, acknowledging that the process isn't the same for every student and doesn't remain the same for each individual, depending upon maturation and the content being learned. We know that students want to progress at a pace that allows them to master new concepts and skills, to access a variety of resources, to receive timely feedback on their progress, to demonstrate their knowledge in multiple ways and to get direction, support and feedback from—as well as collaborate with—experts, teachers, tutors and other students.The result is a growing demand for student-centered, transformative digital learning using competency education as an underpinning.iNACOL released this paper to illustrate the technical requirements and functionalities that learning management systems need to shift toward student-centered instructional models. This comprehensive framework will help districts and schools determine what systems to use and integrate as they being their journey toward student-centered learning, as well as how systems integration aligns with their organizational vision, educational goals and strategic plans.Educators can use this report to optimize student learning and promote innovation in their own student-centered learning environments. The report will help school leaders understand the complex technologies needed to optimize personalized learning and how to use data and analytics to improve practices, and can assist technology leaders in re-engineering systems to support the key nuances of student-centered learning

Developing professional recognition of systems thinking in practice: an interim report

The interim report on developing a competency framework for systems thinking in practice (STiP) provides a step towards possibly developing professional recognition of STiP. The report provides feedback to initial co-respondents involved with phase 1 of this wider inquiry, and provides a platform to a wider audience for initiating a second phase of the inquiry. The phase 1 study had the following objectives: 1. To scope relevant examples of work aimed at giving professional recognition to systems thinking 2. To capture some perspectives on the challenges and opportunities facing the task of giving profession recognition to systems thinking. Phase 2 of the wider inquiry aims to firstly consolidate the findings from phase 1 but also to focus more on moves towards collaborative modelling of a STiP competency framework. The research is carried out by members of the Applied Systems Thinking in Practice (ASTiP) Group at The Open University (UK) with funding from OU eSTEeM (OU Centre for STEM Pedagogy). The research team for phase 1 comprised of Rupesh Shah (Associate Lecturer), who carried out the core research activities, in collaboration with Martin Reynolds (Senior Lecturer) who is overseeing both phases of the wider inquiry, including support for reporting on research outcomes. The findings reported in sections 4, 5 and 6 remain largely unrefined and in sketch (bullet) form at this interim stage of reporting. The interim report comprises a brief background to the wider inquiry before outlining the approach taken to the phase 1 study. The findings are reported in relation to each of the two study objectives. Three themes arising from the study as identified by Rupesh are then discussed. Finally, some concluding ideas are presented for taking forward the outcomes from this study towards a second phase of the inquiry

Mixed Initiative Systems for Human-Swarm Interaction: Opportunities and Challenges

Human-swarm interaction (HSI) involves a number of human factors impacting human behaviour throughout the interaction. As the technologies used within HSI advance, it is more tempting to increase the level of swarm autonomy within the interaction to reduce the workload on humans. Yet, the prospective negative effects of high levels of autonomy on human situational awareness can hinder this process. Flexible autonomy aims at trading-off these effects by changing the level of autonomy within the interaction when required; with mixed-initiatives combining human preferences and automation's recommendations to select an appropriate level of autonomy at a certain point of time. However, the effective implementation of mixed-initiative systems raises fundamental questions on how to combine human preferences and automation recommendations, how to realise the selected level of autonomy, and what the future impacts on the cognitive states of a human are. We explore open challenges that hamper the process of developing effective flexible autonomy. We then highlight the potential benefits of using system modelling techniques in HSI by illustrating how they provide HSI designers with an opportunity to evaluate different strategies for assessing the state of the mission and for adapting the level of autonomy within the interaction to maximise mission success metrics.Comment: Author version, accepted at the 2018 IEEE Annual Systems Modelling Conference, Canberra, Australi