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

Platform-independent and curriculum-oriented intelligent assistant for higher education

Abstract Miscommunication between instructors and students is a significant obstacle to post-secondary learning. Students may skip office hours due to insecurities or scheduling conflicts, which can lead to missed opportunities for questions. To support self-paced learning and encourage creative thinking skills, academic institutions must redefine their approach to education by offering flexible educational pathways that recognize continuous learning. To this end, we developed an AI-augmented intelligent educational assistance framework based on a powerful language model (i.e., GPT-3) that automatically generates course-specific intelligent assistants regardless of discipline or academic level. The virtual intelligent teaching assistant (TA) system, which is at the core of our framework, serves as a voice-enabled helper capable of answering a wide range of course-specific questions, from curriculum to logistics and course policies. By providing students with easy access to this information, the virtual TA can help to improve engagement and reduce barriers to learning. At the same time, it can also help to reduce the logistical workload for instructors and TAs, freeing up their time to focus on other aspects of teaching and supporting students. Its GPT-3-based knowledge discovery component and the generalized system architecture are presented accompanied by a methodical evaluation of the system’s accuracy and performance

Sorption and Mineral-Promoted Transformation of Synthetic Hormone Growth Promoters in Soil Systems

This work examines the fate of synthetic growth promoters (trenbolone acetate, melengestrol acetate, and zeranol) in sterilized soil systems, focusing on their sorption to organic matter and propensity for mineral-promoted reactions. In organic-rich soil matrices (e.g., Pahokee Peat), the extent and reversibility of sorption did not generally correlate with compound hydrophobicity (e.g., Kow values), suggesting that specific binding interactions (e.g., potentially hydrogen bonding through C17 hydroxyl groups for the trenbolone and melengestrol families) can also contribute to uptake. In soils with lower organic carbon contents (1?5.9% OC), evidence supports sorption occurring in parallel with surface reaction on inorganic mineral phases. Subsequent experiments with pure mineral phases representative of those naturally abundant in soil (e.g., iron, silica, and manganese oxides) suggest that growth promoters are prone to mineral-promoted oxidation, hydrolysis, and/or nucleophilic (e.g., H2O or OH?) addition reactions. Although reaction products remain unidentified, this study shows that synthetic growth promoters can undergo abiotic transformation in soil systems, a previously unidentified fate pathway with implications for their persistence and ecosystem effects in the subsurface

Influence of Anionic Cosolutes and pH on Nanoscale Zerovalent Iron Longevity: Time Scales and Mechanisms of Reactivity Loss toward 1,1,1,2-Tetrachloroethane and Cr(VI)

Nanoscale zerovalent iron (NZVI) was aged over 30 days in suspension (2 g/L) with different anions (chloride, perchlorate, sulfate, carbonate, nitrate), anion concentrations (5, 25, 100 mN), and pH (7, 8). During aging, suspension samples were reacted periodically with 1,1,1,2-tetrachloroethane (1,1,1,2-TeCA) and Cr­(VI) to determine the time scales and primary mode of NZVI reactivity loss. Rate constants for 1,1,1,2-TeCA reduction in Cl^–, SO₄^2–, and ClO₄^– suspensions decreased by 95% over 1 month but were generally equivalent to one another, invariant of concentration and independent of pH. In contrast, longevity toward 1,1,1,2-TeCA depended upon NO₃^– and HCO₃^– concentration, with complete reactivity loss over 1 and 14 days, respectively, in 25 mN suspensions. X-ray diffraction suggests that reactivity loss toward 1,1,1,2-TeCA in most systems results from Fe(0) conversion into magnetite, whereas iron carbonate hydroxide formation limits reactivity in HCO₃^– suspensions. Markedly different trends in Cr­(VI) removal capacity (mg Cr/g NZVI) were observed during aging, typically exhibiting greater longevity and a pronounced pH-dependence. Notably, a strong linear correlation exists between Cr­(VI) removal capacities and rates of Fe­(II) production measured in the absence of Cr­(VI). While Fe(0) availability dictates longevity toward 1,1,1,2-TeCA, this correlation suggests surface-associated Fe­(II) species are primarily responsible for Cr­(VI) reduction