TEACHING GRAPHICAL MODELING WITH INTELLIGENT TUTORING SYSTEMS – A REVIEW

Despite the fundamental importance of graphical models across numerous disciplines, educators face significant challenges in teaching advanced concepts in an understandable manner. This paper pro-vides a comprehensive review of 129 studies that have investigated automated assessment systems in graphical modeling education. The review is organized around seven research questions that address the critical needs and challenges of intelligent tutoring systems in this area. The analysis reveals per-sistent shortcomings – such as limited adaptability, fragmented methodologies, and a surprising ab-sence of Large Language Model (LLM) applications – despite their growing importance. Drawing up-on effective approaches identified in the reviewed literature, we offer a set of recommendations that could inform the design of a more dynamic, AI-driven tutoring environment. By incorporating modern technologies such as GPT-4, these recommendations aim to provide adaptive guidance, real-time sup-port, and advanced semantic analysis, thereby overcoming current limitations

Relationships between Social Media Use and Social Isolation among Young Adults

Social isolation, defined as a lack of meaningful social engagement and quality relationships, has severe consequences, including increased risks of depression, loneliness, dementia, poor physical health, and higher mortality rates (Lei et al., 2024). Given its impact, social isolation has become a growing public health concern, particularly among young adults, with one in three reporting significant experiences of social isolation in Australia (VicHealth, 2019). Meanwhile, young people are adopting and relying on social media, including in-app messaging and live chat, etc. This rise in social media use has led to concerns, as excessive and impulsive engagement with these platforms has been positively correlated with social isolation, especially among young adults who are most affected by social media addiction (Santini et al., 2024). Furthermore, certain social media behaviors, such as engaging in upward social comparison, can exacerbate feelings of isolation. Similarly, social media can create unnecessary tension and pressure that can test friendships and potentially destroy relationships, leading to feelings of isolation or misunderstanding (Meshi et al., 2020). Conversely, Siddiq et al. (2024) indicate that social media can provide opportunities to alleviate social isolation. For example, during the COVID-19 pandemic, social media facilitated interpersonal connections when face-to-face interactions were restricted. Additionally, it can foster meaningful relationships and greater community involvement by allowing individuals to share their goals and achievements within their social networks (Jayakody et al., 2022). Given these conflicting perspectives, it is essential to explore how social media use influences social isolation among young adults. Further research should examine this dynamic and identify effective interventions. We shall explore volunteering with the community as an effective strategy to reduce social isolation. Community volunteering may help foster face-to-face interactions and social engagement, ultimately reducing social isolation and enhancing social well-being of young adults

ARE SMART HOME DEVICE CONSUMERS PROTECTED BY THE U.S. STATES DATA PRIVACY LAWS: A CONTENT ANALYSIS

The Internet of Things (IoT), including smart home devices, is seeping into U.S. households, while there are no data privacy laws governing their use in specific. This study seeks to understand the current state of data privacy laws in five U.S. states by carrying out a content analysis focusing on the legislative intent, consumer rights, business rights, enforcement mechanisms, and smart home device regulation. The findings reveal that all five states have similar data privacy laws, with a few variations: California allows consumers a private right of action against companies, whereas consumers in Colorado, Connecticut, Utah, and Virginia have no private right of action against companies, and only the Attorney General can initiate the enforcement proceedings. The study contributes to raising awareness of a right-based approach to managing data privacy, where consumers not only have the right to control their data but also to decide who can use their data

Gender and Student Performance in STEM-Designated Information Systems Courses

This study tested gender-based differences in performance of students in science, engineering, technology, and math (STEM) Information Systems (IS) courses. Data collected from 94 STEM-designated information systems courses (STEM-IS) courses and 2,189 students over a 9-year period were analyzed using ANOVA. This study tested for differences in performances for all the sub-classifications and combinations of the sample such as gender-based differences by course type (technical vs. conceptual) and by course level (graduate vs. undergraduate). The results indicate that female students in STEM-IS courses performed better overall, as well as in all the sub-classifications by course type, undergraduate and graduate course levels, and technical and conceptual course types than the male students. The statistical analysis was followed up with a post hoc analysis of structured interviews of faculty and students in STEM-IS courses to corroborate the results obtained from ANOVA. The importance and implications of the results are discussed

Beyond the Crowd: A Literature Review of AI’s Impact on Crowdsourcing Systems

Crowdsourcing harnesses the collective intelligence of diverse individuals across organizational boundaries. Traditionally, job posters define the task requirements, post jobs on crowdsourcing platforms, and assess the quality of the work. Recent advancements in artificial intelligence have begun to streamline the entire process. Artificial intelligence can assist job posters in clearly defining job requirements, distributing tasks to qualified workers by matching job requirements with their backgrounds, and evaluating the quality of contributions. The advantage of AI in crowdsourcing is its compatibility with various crowdsourcing models (e.g., idea generation) (Dissanayake et al., 2025). In the idea generation model, AI can group and highlight similar generated ideas as submissions roll in and evaluate their quality based on novelty and feasibility in achieving the goals. Similarly, AI can detect abnormalities (e.g., speedy response times) in the microtasking model and calculate the task error rate. Scholars in information systems (IS) and other disciplines have investigated the role of AI in various fields, demonstrating its potential in handling complex tasks. While current research has explored the use of AI in crowdsourcing, the findings are scattered and vary among crowdsourcing models. The literature review of AI in crowdsourcing provides a clear understanding of how AI transforms crowdsourcing operations and identifies research gaps. Therefore, we organize and analyze the current use of AI in crowdsourcing operations and its impact on these operations. Our work employs the Input-Process-Output (IPO) model, a framework widely used in management research, to examine the current application of AI in crowdsourcing. This framework allows us to decompose the crowdsourcing process into three subprocesses: input, process, and output (Ghezzi et al., 2017). The “input” stage involves defining tasks that workers should perform. Building on this foundation, the “process” stage focuses on how job posters manage the crowdsourcing session (e.g., organizing the submissions during the session), which is necessary for the “output” stage, where solutions are evaluated and selected. In a crowdsourcing process, AI plays a unique role, and how and where AI is used in the process determines the outcomes for each stage (e.g., enhanced clarity of job descriptions). Understanding the outcomes of using AI in crowdsourcing can help us assess the impact of AI on the crowdsourcing process. Therefore, we propose the following research questions: “How is AI applied across the stages of a crowdsourcing process among various crowdsourcing models? ” (RQ1) and “What are the impacts of AI usage across the stages of the crowdsourcing process among various crowdsourcing models? ” (RQ2). In conclusion, this literature review offers a clear understanding of the current application of AI in the crowdsourcing process by breaking down the process into three subprocesses, enabling an examination of the nuances of AI\u27s impact on each subprocess

(No) Need to Apply Agile?

The continued popularity of agile information systems development (ISD) underscores its relevance to both researchers and practitioners. At the heart of the agile manifesto is the claim to value people over processes and, as a result, to increase employee job satisfaction. However, the research landscape on this topic is still heterogeneous and lacks a comprehensive overview. This manuscript develops a theoretical framework to guide future studies on job satisfaction in agile ISD. First, the status quo of job satisfaction in agile ISD within information systems and across the disciplines of computer science, psychology, and management is systematically reviewed and critically evaluated. Second, a theoretical framework consisting of three key themes is conceptualized. Third, based on the framework, significant research gaps are identified, and recommendations for future studies of agile ISD at the individual, team, and organizational levels are provided. Finally, strategic directions for the application of agile ISD practices are given

Global Review and Negative Citation System

In an age of exponential research growth and digital dissemination, maintaining the quality and integrity of academic publishing has become increasingly challenging, particularly in the technology and information systems domains. Rising submission volumes, constrained reviewer capacity, and mounting concerns about plagiarism, bias, and unethical authorship practices strain traditional peer review models. Moreover, the lag between submission and publication can leave innovative ideas vulnerable to unauthorized use, especially when rejected manuscripts are inadequately protected. This study introduces a technology-enabled, multi-phase framework that integrates two core concepts: the Global Review model and the Negative Citation mechanism. The Global Review model advocates for an interdisciplinary, semi-open peer review process that combines traditional editorial oversight with transparent, structured dialogue among verified reviewers. Drawing inspiration from existing open peer review systems (e.g., OpenReview, JOSS), our approach enhances accountability through threaded reviewer discussions, dynamic accreditation of reviewers, and flexible options for identity disclosure based on context and risk. An editorial moderation layer is embedded to counterbalance crowd-sourced decision-making, ensuring nuanced evaluations rather than binary up/down voting. The Negative Citation mechanism, while still in development, is conceptualized not as a punitive tool but as a method for post-publication quality monitoring. It provides a formal process for documenting well-substantiated critiques of published work, encouraging methodological rigor, and deterring malpractices without suppressing academic risk-taking. Recognizing ethical concerns, the system includes safeguards such as dispute resolution pathways, structured reporting guidelines, and independent ethical review panels. Grounded in the principles of open science, this model builds on and differentiates itself from existing platforms by emphasizing scalability, reviewer inclusivity, and balanced oversight. This paper critically engages with current literature and platforms, highlights challenges with reviewer incentives, and proposes a phased pilot implementation in conference tracks or special issues to evaluate feasibility and impact. We argue that this integrated framework can foster a more transparent, timely, and ethical research culture—one that not only protects the originality of ideas but also rebuilds trust in the academic publishing system

Experiential Learning in the Metaverse: Implications for Workplace Training

In an era of rapid technological advancement and shifting work modes, the need for innovative, effective, and scalable corporate training solutions has become increasingly important. This paper explores how the Metaverse can serve as a transformative environment offering more personalised, engaging, and effective training experiences. Drawing on Kolb’s Experiential Learning Theory (ELT), the study examines how the elements of the Metaverse — such as immersion, interactivity, and persistence — can enhance skill acquisition, knowledge retention, employee engagement, and organisational learning outcomes. Through the analysis of nine case studies across sectors (telecommunications, retail, hospitality, manufacturing, consulting and social services), we identify three dominant models of Metaverse-based training: risk-free learning environments, digital twin integration, and immersive skill development platforms. The findings highlight the ability of these training programs to enable experiences that support both technical and interpersonal skill development. Benefits include reduced training time, increased learner satisfaction, improved performance metrics, and global scalability. However, successful implementation is contingent upon addressing critical challenges including infrastructure demands, content development, accessibility, employee adaptation, health implications, and ethical concerns such as data privacy and inclusivity