Advances In Artificial Intelligence Research Articles

Sustaining Ethical ePortfolio Practices: Insights from AAEEBL’s Task Force Year 4

This paper recognises the importance of digital ethics when using ePortfolios and provides information about the development of a comprehensive resource from the Association for Authentic, Experiential and Evidence-based Learning (AAEEBL) Digital Ethics in ePortfolios Task Force’s four-year work. With the public release of ChatGPT addressing ethical challenges in education is critical. Over three years, the global Task Force diligently drafted, refined, and published a suite of digital ethics principles, strategies, scenarios, and resources, which is available as open source. In the fourth year (July 2022-June 2023), the Task Force formed three sub-groups to manage the growing number of objectives. The Development group explored intersections between principles and generative artificial intelligence (GenAI), while evaluating data responsibility. The Outreach group disseminated the work through engaging workshops and publications, which also allowed the wider community feedback opportunities to further enhance the resources. Furthermore, the Research group undertook the ePortfolio Mapping Project to shed light on labour dynamics within ePortfolio practice across North America. The survey instrument was then adapted for data collection in Australia, and New Zealand. The paper highlights the significant achievements of these collective efforts, shares preliminary research findings, and offers valuable insights. It aims to promote ethical ePortfolio usage to enhance teaching, learning and assessment. Practitioner Notes Recognising potential ethical challenges in digital learning is critical. ePortfolios enable integrated learning experiences to meet a range of pedagogical objectives, including the tracking of learning processes as well as end artefacts. Learning how to apply ethical decision-making in using ePortfolios is essential. Comprehensive resources have been developed to assist educators, students, professional staff, and other digital pedagogy stakeholders. Research is underway into current ePortfolio practices which will inform future thinking, advocacy, and awareness.

Language Styles, Recovery Strategies and Users’ Willingness to Forgive in General Artificial Intelligence Service Recovery: A Mixed Study

As the prevalence of generative artificial intelligence (GenAI) in the service sector continues to grow, the impact of the language style and recovery strategies utilized during service failures remains insufficiently explored. This study, grounded in the theory of social presence and dual-process theory, employed a mixed-method approach combining questionnaire surveys and event-related potential (ERP) experiments to investigate the effect of different language styles (rational vs. humorous) and recovery strategies (gratitude vs. apology) on users’ willingness to forgive during the GenAI service recovery process. It further delves into the chained mediating role of perceived sincerity and social presence in this process. The findings revealed that a humorous language style was more effective in enhancing users’ willingness to forgive compared to a rational style, primarily through the enhancement of users’ perceived sincerity and sense of social presence; recovery strategies played a moderating role in this process, with the positive impact of perceived sincerity on social presence being significantly amplified when the GenAI service adopted an apology strategy. ERP results indicated that a rational language style significantly induced a larger N2 component (cognitive conflict) in apology scenarios, while a humorous style exhibited higher amplitude in the LPP component (positive emotional evaluation). This research unveils the intricate relationships between language style, recovery strategies, and users’ willingness to forgive in the GenAI service recovery process, providing important theoretical foundations and practical guidance for designing more effective GenAI service recovery strategies, and offering new insights into developing more efficacious GenAI service recovery tactics.

Effectiveness of Generative AI for Post-Earthquake Damage Assessment

After an earthquake, rapid assessment of building damage is crucial for emergency response, reconstruction planning, and public safety. This study evaluates the performance of various Generative Artificial Intelligence (GAI) models in analyzing post-earthquake images to classify structural damage according to the EMS-98 scale, ranging from minor damage to total destruction. Correct classification rates for masonry buildings varied from 28.6% to 64.3%, with mean damage grade errors between 0.50 and 0.79, while for reinforced concrete buildings, rates ranged from 37.5% to 75.0%, with errors between 0.50 and 0.88. Fine-tuning these models could substantially improve accuracy. The practical implications are significant: integrating accurate GAI models into disaster response protocols can drastically reduce the time and resources required for damage assessment compared to traditional methods. This acceleration enables emergency services to make faster, data-driven decisions, optimize resource allocation, and potentially save lives. Furthermore, the widespread adoption of GAI models can enhance resilience planning by providing valuable data for future infrastructure improvements. The results of this work demonstrate the promise of GAI models for rapid, automated, and precise damage evaluation, underscoring their potential as invaluable tools for engineers, policymakers, and emergency responders in post-earthquake scenarios.

Visualization of AI Accuracy: A Novel Assignment for the Teaching of Critical Thinking and Science Writing

Background Rapid changes brought on by generative artificial intelligence (AI) have emphasized the need to teach students to work with this technology while also developing the “robot proof” human skills future workers will need, such as creativity, communication, and critical thinking. Objective The study objective was to explore whether a fact-checking, generative-AI assignment, inserted between the outline, and first-draft stages of a student's literature review writing process, would relate to student classification, perceptions of AI accuracy, and future trust in AI-generated content. Method Students in upper and lower division psychology classes used AI to generate a literature review on their final paper topic, which they then fact-checked for accuracy and usefulness using a color-coded system. Results Lower division students expected more inaccuracy, highlighted less information as inaccurate, and reported greater future trust of AI-generated content than upper division students. Conclusion Students with more experience critically evaluating primary sources may be better equipped to detect inaccuracies within AI-generated content. Teaching Implications Teachers of any course requiring a literature review paper may use this assignment to encourage student use of AI with a critical eye toward recognizing where that content is incorrect.

Using Generative AI to Promote Psychological, Feedback, and Artificial Intelligence Literacies in Undergraduate Psychology

Background With the arrival of generative artificial intelligence (genAI) tools, psychology educators are rethinking their assessment practices. Objective This paper describes one approach to integrating genAI into an assessment designed to promote psychological literacy. Method Students used ChatGPT to generate a media release about a published article and then wrote a critique. We evaluated whether students were able to use the marking rubric to assess the ChatGPT output, and whether working with the rubric early in the assessment process had benefits for their grades on subsequent tasks. Results The results show that students accurately assessed the ChatGPT output against the marking rubric, judging the output to be stylistically good but lacking in accurate coverage of the aims, methods, and results of the research. Working with genAI and the marking rubric early in the assessment process had benefits for performance, relative to cohorts that had engaged in peer review. Conclusion By allowing students to use genAI and scaffolding the process of critiquing and revising, students gained competencies in psychological, feedback, and AI literacies. Teaching implications Integrating genAI presents opportunities for learning, if educators can think beyond the artifact and design assessment that allows our students to showcase their learning process.

Who Gets Paid (for) What? The Cultural Political Economy of News Content in Generative AI

One of the key controversies that generative artificial intelligence (AI) has recently stirred was whether compensation is due for the copyrighted materials used to train AI models. This article explores the logic, trajectories, and dynamics of content generation, including news, through generative AI in two distinctive yet intertwined domains. Guided by a cultural political economy approach, it examines how both the political context (validation/legitimation of AI-generated news content by established news media) and the economic context (use of unpaid and underpaid labor in the forms of freely scraped data and data annotation work) shape the deployment of news content on AI models. It further untangles how the space for serious, independent journalism may shrink, as big tech companies’ algorithmic technologies emerge as a solution to contemporary problems in journalism. A clear danger here is that AI companies’ proprietary algorithms, language training models, and value-laden parameters are incompatible with journalism's democratic obligations and responsibilities.

Understanding EFL teachers’ experiences of ChatGPT-driven collaborative reflective practice through a community of practice lens

ABSTRACT Purpose: Despite the acknowledged impact of generative artificial intelligence chatbot (ChatGPT) in the field of education, further research is necessary to explore the potential of ChatGPT in enhancing the professional growth of English as a foreign language (EFL) teachers. Hence, this study aims to uncover how Iranian EFL teachers’ (four novice and four experienced teachers) ChatGPT-driven collaborative reflective practice (CRP), both independently and collaboratively, can contribute to their professional development. Design/methodology/approach: We adopted a transcendental phenomenology approach which encouraged us to eliminate any biases or preconceived notions we may have had regarding the phenomenon under investigation and analyzed the data thematically. Findings: Findings gathered through group discussions, observations, and semi-structured interviews demonstrated that EFL teachers developed professionally through ChatGPT-driven CRP done individually and collaboratively. Teachers’ individual reflection with ChatGPT served as a complementary preparatory step for contributing insightful perspectives during CRP in the community of practice (CoP). Originality/value: Discussion of findings indicates the ways ChatGPT-driven CRP as a new mode can enhance the quality of language teacher education and professional development programs, especially in disadvantaged contexts where mentors and teacher educators are not always available to provide language teachers with reflective feedback.

AI Ethics in Journalism (Studies): An Evolving Field Between Research and Practice

The integration of artificial intelligence (AI) in journalism has sparked complex ethical debates, particularly with the rise of generative AI systems. By now, AI permeates the entire news cycle, from information gathering to news dissemination, raising questions revolving around issues such as transparency, accountability, responsibility, bias, and diversity. Previous research showed that news organizations have slowly approached and adapted to ethical concerns regarding the use of AI, developing critical stances mainly due to rising AI power, growing audience skepticism, and mounting tensions within the industry between news publishers’ strategies and journalists’ anxieties. Consequently, ethical guidelines have started to emerge in news organizations, but their practical application remains challenging and under-studied, not only due to the opacity of AI algorithms, but also due to the difficulties of “embedding” journalistic values into AI systems. In the light of an intensifying discourse about ethical concerns in the news industry and growing efforts by governments and institutions such as the European Union to strengthen AI governance, journalism studies have started to explore the issue as well. However, research on AI ethics is still in its infancy, with significant gaps in understanding the practical enforcement of ethical guidelines within newsrooms, in particular when it comes to the design of AI systems. This essay critically discusses the way journalism (studies) approach ethical issues related to the use and the design of AI systems, given that the responsible use and design of AI systems in journalism is crucial given its integral role for democracy and society.

Digital deception: generative artificial intelligence in social engineering and phishing

The advancement of Artificial Intelligence (AI) and Machine Learning (ML) has profound implications for both the utility and security of our digital interactions. This paper investigates the transformative role of Generative AI in Social Engineering (SE) attacks. We conduct a systematic review of social engineering and AI capabilities and use a theory of social engineering to identify three pillars where Generative AI amplifies the impact of SE attacks: Realistic Content Creation, Advanced Targeting and Personalization, and Automated Attack Infrastructure. We integrate these elements into a conceptual model designed to investigate the complex nature of AI-driven SE attacks—the Generative AI Social Engineering Framework. We further explore human implications and potential countermeasures to mitigate these risks. Our study aims to foster a deeper understanding of the risks, human implications, and countermeasures associated with this emerging paradigm, thereby contributing to a more secure and trustworthy human-computer interaction.

Enriching Social Cognitive and Behavioral intention by Mobilizing Robotics Tutor-based GenAI in Education 6.0

The study aims to investigate the impact of incorporating robotics-based GenAI into pedagogical approaches, in order to enrich social cognitive development in the higher education context. By examining the impact of robotics-based Generative Artificial Intelligence (GenAI) on social cognition and learning outcomes, this study investigates the application of GenAI in Indonesian higher education, specifically in sociology programs. In poor countries like Indonesia, where pedagogical and infrastructural constraints prevent its widespread adoption, the report highlights the gap in the application of GenAI in the education system. Over the course of four weeks, 27 sociology students used ChatGPT as a robot tutor for undergraduate students to access information, and the study used a mixed-methods approach to collect data through observation and structured questionnaires. Despite the students' perception of GenAI as a valuable tool for enhancing engagement, problem-solving, and comprehension of intricate sociology topics, the findings revealed no correlation between the use of ChatGPT and students' social cognition. Furthermore, it appears that classroom dynamics and teacher engagement are more important factors influencing undergraduates’ social cognitive development. Future recommendations to fully incorporate GenAI into education and investigate its long-term benefits include strengthening infrastructure, addressing ethical issues, and improving teacher training.

What should an ordinary person understand about the work of generative artificial intelligence? Proceedings of the competition "TRIZformashka-2024"

Three basic aspects of the operation of generative neural network models (generative artificial intelligence) are discussed in the article: the concept of a "token", the probabilistic nature of the generated response, and the concept of a "large model", the size of which ensures the pseudo-intelligent behavior of neural network chatbots. The issues of implementing generative models, areas and methods of their application are not discussed in principle.The materials of the "TRIZformashka-2024" competition, which was dedicated to neural network models, are provided. The fact of pseudo-intelligence of generative models is demonstrated. It turns out that a model trained on a single phrase "mama myla ramu" ("mama washed the frame") and using a context of a single letter for generation can sometimes behave as if it knows the rules of declension in the Russian language and is able to change a word by case!The concept of a "token" is considered in relation to the generation of texts, pictures and passwords. On the basis of "tokens" a practically useful method of generating passwords is built, difficult to guess, but easy to reproduce (difficult to forget).The concept of a "large model" is presented clearly and intelligibly due to its "visualization" by comparing it with physical quantities. (If one parameter of a neural network weighed one gram, then 200 freight trains would be needed to transport it. If one parameter had a length one millimeter, then the neural network would revolve around the Earth at the equator 25 times. If one second was required to learn one parameter, then it would be necessary to start training a modern neural network in the times of the Cro-Magnons.)The materials will be useful for studying generative artificial intelligence at any age.

Expanding experiences and anxieties: gender-identity and sexual-orientation differences in attitudes toward synthetic pornography

ABSTRACT Media-technology innovations have long been animated by demand for sexual content, and generative artificial intelligence (AI) is no exception. Synthetic pornography (SP) (distinct from deepfakes) comprises machine-generated visual media depicting non-actual bodies engaging in sexual activities. SP represents a complex convergence of pornography and AI with their historical challenges and opportunities for people of varied gender identities and sexual orientations. This descriptive study offers a baseline account of attitudes toward SP compared across social groups. Leveraging an online survey and a clear definition of SP, we observed quantitative and qualitative differences in attitudes among people with differing gender identities and sexual orientations. The findings indicate idiosyncratic concerns and hopes that may be interpretable in relation to gendered anxieties; observations indicate that attitudes toward SP may not be stable or well formed at this stage in the technology’s emergence.

Comment on: Assessing the Quality and Readability of Online Patient Information: ENT UK Patient Information e-Leaflets vs Responses by a Generative Artificial Intelligence.

Comment on: Assessing the Quality and Readability of Online Patient Information: ENT UK Patient Information e-Leaflets vs Responses by a Generative Artificial Intelligence.

The risk assessment of public-private partnership projects using artificial intelligence algorithms

Purpose: is to develop an innovative approach to risk management in public-private partnership (PPP) projects using advanced artificial intelligence technologies that allow creating the risk assessment model that takes into account non-linear relationships between various risk factors.Methods: in addition to traditional methods of scientific knowledge, interdisciplinary approaches of risk management and established practice of machine learning were used in the work. The methodological basis of the study was formed by works on the risk assessment and the application of AI algorithms in this area. The empirical basis of the study was the data of the official portal of ROSINFRA on public-private partnership projects.Results: the practice of applying AI algorithms to the task of assessing the risks of PPP-projects in Russia and abroad was studied. It is established that the most effective result is shown by the models based on Random-Forest-Classifier. However, the presented solutions do not take into account Russian economic realities. The authors have structured a database of implemented PPP-projects suitable for risk modeling. The model for assessing the risk of failure to achieve the objectives of Russian PPP-projects has been developed and its quality has been assessed. Recommendations on implementation of the model in the operational loop of PPP projects realization processes are offered.Conclusions and Relevance: the developed model allows, according to the general parameters of PPP project (region, authority, term of agreement, industry and scope of implementation, etc.) with the accuracy of 93% (according to the ROC\AUC metric), to assess the risk that the project will end incorrectly (due to a failed tender, refusal to launch, termination by court decision, cancellation/annulment of the tender). With the help of the model the executive authorities of the Russian Federation can build risk management for PPP projects management in the regions and thus contribute to their efficiency improvement. The article may also become useful for project management practitioners and appraisers.

Advanced artificial intelligence framework for T classification of TNM lung cancer in 18FDG-PET/CT imaging

The integration of artificial intelligence (AI) into lung cancer management offers immense potential to revolutionize diagnostic and treatment strategies. The aim is to develop a resilient AI framework capable of two critical tasks: firstly, achieving accurate and automated segmentation of lung tumors and secondly, facilitating the T classification of lung cancer according to the ninth edition of TNM staging 2024 based on PET/CT imaging. This study presents a robust AI framework for the automated segmentation of lung tumors and T classification of lung cancer using PET/CT imaging. The database includes axial DICOM CT and18FDG-PET/CT images. A modified ResNet-50 model was employed for segmentation, achieving high precision and specificity. Reconstructed 3D models of segmented slices enhance tumor boundary visualization, which is essential for treatment planning. The Pulmonary Toolkit facilitated lobe segmentation, providing critical diagnostic insights. Additionally, the segmented images were used as input for the T classification using a CNN ResNet-50 model. Our classification model demonstrated excellent performance, particularly for T1a, T2a, T2b, T3 and T4 tumors, with high precision, F1 scores, and specificity. The T stage is particularly relevant in lung cancer as it determines treatment approaches (surgery, chemotherapy and radiation therapy or supportive care) and prognosis assessment. In fact, for Tis-T2, each increase of one centimeter in tumor size results in a worse prognosis. For locally advanced tumors (T3-T4) and regardless of size, the prognosis is poorer. This AI framework marks a significant advancement in the automation of lung cancer diagnosis and staging, promising improved patient outcomes.

Evaluating Use of Generative Artificial Intelligence in Clinical Pathology Practice: Opportunities and the Way Forward.

Generative artificial intelligence (GAI) technologies are likely to dramatically impact health care workflows in clinical pathology (CP). Applications in CP include education, data mining, decision support, result summaries, and patient trend assessments. To review use cases of GAI in CP, with a particular focus on large language models. Specific examples are provided for the applications of GAI in the subspecialties of clinical chemistry, microbiology, hematopathology, and molecular diagnostics. Additionally, the review addresses potential pitfalls of GAI paradigms. Current literature on GAI in health care was reviewed broadly. The use case scenarios for each CP subspecialty review common data sources generated in each subspecialty. The potential for utilization of CP data in the GAI context was subsequently assessed, focusing on issues such as future reporting paradigms, impact on quality metrics, and potential for translational research activities. GAI is a powerful tool with the potential to revolutionize health care for patients and practitioners alike. However, GAI must be implemented with much caution considering various shortcomings of the technology such as biases, hallucinations, practical challenges of implementing GAI in existing CP workflows, and end-user acceptance. Human-in-the-loop models of GAI implementation have the potential to revolutionize CP by delivering deeper, meaningful insights into patient outcomes both at an individual and population level.

Artificial general intelligence for the upstream geoenergy industry: A review

Artificial General Intelligence (AGI) is set to profoundly impact the traditional upstream geoenergy industry (i.e., geothermal energy, oil and gas industry) by introducing unprecedented efficiencies and innovations. This paper explores AGI's foundational principles and its transformative applications, particularly focusing on the advancements brought about by large language models (LLMs) and extensive computer vision systems in the upstream sectors of the industry. The integration of Artificial Intelligence (AI) has already begun reshaping the upstream geoenergy landscape, offering enhancements in production optimization, downtime reduction, safety improvements, and advancements in exploration and drilling techniques. These technologies streamline logistics, minimize maintenance costs, automate monotonous tasks, refine decision-making processes, foster team collaboration, and amplify profitability through error reduction and actionable insights extraction. Despite these advancements, the deployment of AI technologies faces challenges, including the necessity for skilled professionals for implementation and the limitations of model training on constrained datasets, which affects the models' adaptability across different contexts. The advent of generative AI, exemplified by innovations like ChatGPT and the Segment Anything Model (SAM), heralds a new era of high-density innovation. These developments highlight a shift towards natural language interfaces and domain-knowledge-driven AI, promising more accessible and tailored solutions for the upstream geoenergy industry. This review articulates the vast potential AGI holds for tackling complex operational challenges within the upstream geoenergy industry, requiring near-human levels of intelligence. We discussed the promising applications, the hurdles of large-scale AGI model deployment, and the necessity for domain-specific knowledge in maximizing the benefits of these technologies.

Generative AI in Science Education: A Learning Revolution or a Threat to Academic Integrity? A Bibliometric Analysis

The integration of generative artificial intelligence (AI) in Science, Technology, Engineering, and Mathematics (STEM) education presents transformative opportunities alongside significant challenges. This study investigates the dual impact of generative AI on STEM learning outcomes and academic integrity through a comprehensive bibliometric analysis employing co-citation, keyword analysis, and trend mapping. The results reveal that AI tools such as ChatGPT have revolutionized personalized learning by offering tailored feedback, enhancing critical thinking, and improving student engagement. However, these advancements are tempered by concerns over academic misconduct, particularly plagiarism, and the erosion of essential cognitive skills due to overreliance on AI-generated content. Ethical considerations remain critical, necessitating the development of robust policies and ethical frameworks to safeguard academic integrity. Beyond educational settings, the findings suggest broader applicability to professional training and skills development, as the benefits and challenges of AI extend beyond coursework. This research provides valuable insights for educators, policymakers, and researchers, advocating for a balanced approach to AI integration that maximizes its potential while preserving educational standards.

A Novel Cognitive Behavioral Therapy-Based Generative AI Tool (Socrates 2.0) to Facilitate Socratic Dialogue: Protocol for a Mixed Methods Feasibility Study.

Digital mental health tools, designed to augment traditional mental health treatments, are becoming increasingly important due to a wide range of barriers to accessing mental health care, including a growing shortage of clinicians. Most existing tools use rule-based algorithms, often leading to interactions that feel unnatural compared with human therapists. Large language models (LLMs) offer a solution for the development of more natural, engaging digital tools. In this paper, we detail the development of Socrates 2.0, which was designed to engage users in Socratic dialogue surrounding unrealistic or unhelpful beliefs, a core technique in cognitive behavioral therapies. The multiagent LLM-based tool features an artificial intelligence (AI) therapist, Socrates, which receives automated feedback from an AI supervisor and an AI rater. The combination of multiple agents appeared to help address common LLM issues such as looping, and it improved the overall dialogue experience. Initial user feedback from individuals with lived experiences of mental health problems as well as cognitive behavioral therapists has been positive. Moreover, tests in approximately 500 scenarios showed that Socrates 2.0 engaged in harmful responses in under 1% of cases, with the AI supervisor promptly correcting the dialogue each time. However, formal feasibility studies with potential end users are needed. This mixed methods study examines the feasibility of Socrates 2.0. On the basis of the initial data, we devised a formal feasibility study of Socrates 2.0 to gather qualitative and quantitative data about users' and clinicians' experience of interacting with the tool. Using a mixed method approach, the goal is to gather feasibility and acceptability data from 100 users and 50 clinicians to inform the eventual implementation of generative AI tools, such as Socrates 2.0, in mental health treatment. We designed this study to better understand how users and clinicians interact with the tool, including the frequency, length, and time of interactions, users' satisfaction with the tool overall, quality of each dialogue and individual responses, as well as ways in which the tool should be improved before it is used in efficacy trials. Descriptive and inferential analyses will be performed on data from validated usability measures. Thematic analysis will be performed on the qualitative data. Recruitment will begin in February 2024 and is expected to conclude by February 2025. As of September 25, 2024, overall, 55 participants have been recruited. The development of Socrates 2.0 and the outlined feasibility study are important first steps in applying generative AI to mental health treatment delivery and lay the foundation for formal feasibility studies. DERR1-10.2196/58195.

Strong and weak AI narratives: an analytical framework

Strong and weak AI narratives: an analytical framework