Reinforcement Learning in Behavior Science and Artificial Intelligence

Machine ethics has received increasing attention over the past few years because of the need to ensure safe and reliable artificial intelligence (AI). The two dominantly used theories in machine ethics are deontological and utilitarian ethics. Virtue ethics, on the other hand, has often been mentioned as an alternative ethical theory. While this interesting approach has certain advantages over popular ethical theories, little effort has been put into engineering artificial virtuous agents due to challenges in their formalization, codifiability, and the resolution of ethical dilemmas to train virtuous agents. We propose to bridge this gap by using role-playing games riddled with moral dilemmas. There are several such games in existence, such as Papers, Please and Life is Strange, where the main character encounters situations where they must choose the right course of action by giving up something else dear to them. We draw inspiration from such games to show how a systemic role-playing game can be designed to develop virtues within an artificial agent. Using modern day AI techniques, such as affinity-based reinforcement learning and explainable AI, we motivate the implementation of virtuous agents that play such role-playing games, and the examination of their decisions through a virtue ethical lens. The development of such agents and environments is a first step towards practically formalizing and demonstrating the value of virtue ethics in the development of ethical agents.

Artificial intelligence is penetrating various fields. The demand for interdisciplinary talent is increasingly important, while interdisciplinary educational activities for high school students are lagging behind. Project‐based learning (PBL) in artificial intelligence (AI) and robotic education activities supported by a robotic sailboat platform, the sailboat test arena (STAr), has been shown to popularise AI and robotic knowledge in young students. In the implementation of the programme, PBL was provided for students, and gamification pedagogy was applied to increase participants' learning motivation and engagement. The results show that the proposed STAr‐based programme is capable of delivering the desired knowledge and skills to students at high school levels. The assessment results suggest that most students achieve learning outcomes on average. Students showed more interest in AI and marine disciplines and were willing to participate in more such educational programs. The findings fill the research gap that few existing education platforms have facilitated the teaching and learning of AI and marine disciplines for high school students.

This book offers a unique interdisciplinary perspective on the ethics of 'artificial intelligence' – autonomous, intelligent, (and connected) systems, or AISs, applying principles of social cognition to understand the social and ethical issues associated with the creation, adoption, and implementation of AISs. As humans become entangled in sociotechnical systems defined by human and artificial agents, there is a pressing need to understand how trust is created, used, and abused. Compounding the difficulty in answering these questions, stakeholders directly or indirectly affected by these systems differ in their motivations, understanding, and values. This volume provides a comprehensive resource to help stakeholders understand ethical issues of designing and implementing AISs using an ethical sensemaking approach. Starting with the general technical affordances of AIS, Dr. Jordan Richard Schoenherr considers the features of system design relating data integrity, selection and interpretation of algorithms, and the evolution processes that drive AISs innovation as a sociotechnological system. The poles of technophobia (algorithmic aversion) and technophilia (algorithmic preference) in the public perception of AISs are then described and considered against existing evidence, including issues ranging from the displacement and re-education needs of the human workforce, the impact of use of technology on interpersonal accord, and surveillance and cybersecurity. Ethical frameworks that provide tools for evaluating the values and outcomes of AISs are then reviewed, and how they can be aligned with ethical sensemaking processes identified by psychological science is explored. Finally, these disparate threads are brought together in a design framework. Also including sections on policies and guideline, gaming and social media, and Eastern philosophical frameworks, this is fascinating reading for students and academics in psychology, computer science, philosophy, and related areas, as well as professionals such as policy makers and those working with AI systems.

Medical imaging and radiation science students' use of artificial intelligence for learning and assessment

The dynamic nature of Environmental, Social, and Governance (ESG) audit strategies, particularly in responding to evolving environmental regulations and shifting corporate sustainability practices, necessitates robust methodological innovation. Reinforcement learning (RL) presents a transformative pathway for refining ESG audit processes through continuous interaction with environmental performance data, regulatory updates, and real-time ecological compliance feedback. Nevertheless, the application of RL to optimize ESG auditing remains significantly underexplored. Addressing this gap, our study develops an RL-driven model designed to strategically recalibrate ESG auditing mechanisms, with enhanced focus on responsiveness to emerging environmental compliance requirements and ecological risk factors. We adopt a dual-method research framework integrating theoretical and empirical approaches. The theoretical investigation establishes the structural compatibility between RL algorithms and environmental ESG audit optimization, ensuring alignment with the complexities of sustainability decision-making. Empirical validation employs: (1) large-scale simulations using synthetic corporate environmental datasets to evaluate model performance across diverse operational scenarios, and (2) real-world applications to quantify the model's efficacy in improving ecological audit efficiency, mitigating environmental compliance risks, and addressing the challenges of modern sustainability auditing. Results demonstrate that the RL-driven model outperforms conventional methods in adapting to environmental data variability, achieving a 40% increase in resource efficiency and a 30% improvement in predicting ecological compliance risks. Practical implementations further reveal a 25% reduction in audit cycle duration and significantly fewer errors in environmental disclosure assessments. These findings highlight RL's potential to revolutionize environmentally focused ESG auditing while underscoring ongoing challenges in data reliability and model interpretability for sustainability applications.

As the popularity and accessibility of artificial intelligence (AI) in learning continue to rise, online education has increasingly incorporated AI. It is imperative to investigate how AI in the scholarly community supports the learning of autonomous students. This scoping review investigated articles that employed AI in discussion forums. The objective of this study is to develop a summary that denotes the effective integration of AI in discussion forums, thereby characterizing the overall learning experience. In accordance with the search, appraisal, synthesis, and analysis (SALSA) framework, the preferred reporting items for systematic reviews and meta-analyses (PRISMA) flow diagram was employed to design the article screening and selection process. Scopus was utilized for the literature search, resulting in the identification of twelve articles that met the predetermined inclusion and exclusion criteria. The extracted articles covered the following six applications: intelligent tutoring systems, AI-assisted apps, chatbots, automatic formative assessment, AI teaching assistants, and AI-assisted lifelong learning assistants. Three types of man-machine partnerships related to the activities are discussed: student-AI partnerships, student-AI-student partnerships, and student-AI-teacher partnerships, the latter being the most commonly observed. The generated content was frequently processed through teacher adjustments and checks. Components of community of inquiry theory were adapted as indicators. Cognitive presence was observed to be more predominant than social presence, with a particular emphasis on individual learning achievements. Three distinct perspectives regarding the role of AI in learning were identified. The first perspective posited that AI functioned as a virtual teacher, supplementing the teaching functions of human teachers and delivering information to students. In the second perspective, AI facilitated students in collecting information by enabling interactions with messages or peers. In the third perspective, the significance of the automatic evaluation function, which assessed learning activities and provided feedback to enhance the system while simultaneously implementing immediate changes in the learning process, was acknowledged. Regarding the role of communication, teachers clearly intended for students to acquire knowledge through it. Despite the focus on assisting individual learners, each article encompassed references from a wide range of fields and exhibited substantial diversity overall. Consequently, the domain of discussion forums with AI was depicted through the complex interplay between technology, pedagogy, and the learning environment. This context is known as entangled pedagogies, which emphasize understanding the interconnections among these elements and their reciprocal influences. In summary, AI was predominantly utilized in the form of chatbots and GenAI in discussion forums. Emerging AI roles included virtual teachers, peer/message matching, and automated evaluation. Aggregated information was presented in a manner that supported student learning. AI also served as a conduit between students and teachers.

Learning motivated by electrical stimulation of the brain.

Like many construction industry professions, quantity surveying (QS) has been around for many years and has undergone many changes to reflect developments within the wider industry and society. The proliferation of computers into the design process starting from the 1980’s leading up to the rise of Building Information Modelling has particularly led to significant changes in the design and construction landscape. In the UK for instance, the proliferation of BIM and possible demise of traditional Bill of Quantities, with the concurrent rise of smart buildings/cities with exploitation of Big Data (BD), artificial intelligence (AI) and Machine Learning (ML). It implies that QS practices need to reflect on emerging products and services that can promote construction quality and productivity as well as their own professional development. With the decline of traditional QS roles and increased focus on speed of construction, there may be opportunities for different roles for quantity surveyors when dealing with data-driven needs of advanced clients such as BIM managers and Project managers. Additionally, there is need to improve the market value for traditional QS practices when dealing with less innovative clients with less time constraints, which inadvertently contributes to a skills gap which will allow practices to charge more for the traditional services. This study is an exploratory research based on secondary data, which is aim at understanding BIM adoption and related technical advancements that represent innovative and emerging roles for QS professionals to meet the growing demands in the industry. The findings will ignite and support the need for changes in practice, professional education as well as attitudinal behaviour required toward the UK’s Construction 2025 goals. This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Applied Science University Journal of Natural Sciences following peer review. The definitive publisher-authenticated version Seidu et al (2020) Innovative Changes in Quantity Surveying Practice through BIM, Big Data, Artificial Intelligence and Machine Learning, is available online at: DOI:10.18576/asuj/040105

The rapid expansion of big data has transformed research practices across disciplines, yet disparities exist in its adoption among U.S. institutions of higher education. This study examines trends in NSF-funded big data-driven research across research domains, institutional classifications, and directorates. Using a quantitative approach and natural language processing (NLP) techniques, we analyzed NSF awards from 2006 to 2022, focusing on seven NSF research areas: Biological Sciences, Computer and Information Science and Engineering, Engineering, Geosciences, Mathematical and Physical Sciences, Social, Behavioral and Economic Sciences, and STEM Education (formally known as Education and Human Resources). Findings indicate a significant increase in big data-related awards over time, with CISE (Computer and Information Science and Engineering) leading in funding. Machine learning and artificial intelligence are dominant themes across all institutions’ classifications. Results show that R1 and non-minority-serving institutions receive the majority of big data-driven research funding, though HBCUs have seen recent growth due to national diversity initiatives. Topic modeling reveals key subdomains such as cybersecurity and bioinformatics benefiting from big data, while areas like Biological Sciences and Social Sciences engage less with these methods. These findings suggest the need for broader support and funding to foster equitable adoption of big data methods across institutions and disciplines.

This study examines students' perceptions of the positive and negative impacts of using Artificial Intelligence (AI) in learning in the digital era. In the digital era, AI is growing rapidly and its potential is expanding in education. Understanding students' perceptions is key to implementing AI effectively and optimally. This study used a qualitative method with a descriptive approach and involved 114 students at two universities in Lampung. Data was collected through an online questionnaire. The results show that most students have a positive perception of the impact of AI in learning. AI helps them focus on learning, understand the material more easily, complete tasks faster, and make learning more interesting. However, some students also have concerns about the negative impacts of AI, such as laziness in learning, dependence on technology, and potential misuse of AI. This study concludes that AI has great potential to improve the quality of student learning. It should be understood that AI cannot replace the role of lecturers, but it can assist lecturers and students in the learning process. It is important to use AI carefully and balance it with conventional learning. The results of this research are expected to contribute to various parties in achieving better educational goals.

The STEAM-CT approach integrates Science, Technology, Engineering, Arts, and Mathematics with Computational Thinking (CT) to help students learn how to think, design, and solve problems. It gives students hands-on, interdisciplinary experiences where they apply logic and creativity through real-world applications. The purpose of this study is to foster the development of computational thinking among Deaf students by embedding Artificial Intelligence (AI) learning within a STEAM-CT approach. This learning program consisted of three main phases: (1) exploring AI processes and tools, (2) constructing an AI system, and (3) designing AI-driven innovations. Thirty-six Deaf students from seven Deaf schools participated in this program, which aims to enhance their CT abilities and cultivate their capacity to create AI-based solutions. Students’ progress was measured using a CT framework encompassing knowledge of concepts, applied practices and perspectives. Assessments included multiple-choice tests for CT concepts, task-based rubrics for CT practices, and interviews for CT perspectives. The results showed that Deaf students gained a better understanding of CT concepts, demonstrated advanced CT practices, and exhibited strong CT perspectives. These findings suggest that AI learning through a STEAM-CT approach can effectively promote Deaf students’ computational thinking abilities.

The use of Artificial Intelligence (AI) in student learning is an increasingly relevant issue in the context of modern education. This article outlines the main issues by explaining the challenges and potential of AI in learning. The purpose of this article is to provide acomprehensive understanding of the role of AI in student learning in Indonesia. The research method used is a literature review, which includes the analysis of various sources and perspectives related to theuse of AI in higher education. The data used in this article is information from various literature sources, including research results, scientific articles, and news related to the implementation of AI in education. Examples of AI applications, such as chatbots for academic guidance, online learning systems, and automated assessments, were obtained from these sources. The results of the data analysis show thatthe use of AI in student learning has great potential to improve the quality of education, but it is necessary to pay attention to ethical challenges, the risk of dependency, and the important role of humaninteraction. Solutions such as AI ethics education, strict regulations, and the integration of AI with human interaction become integral parts of the conclusion of this article. In the era of digital transformation, adeep understanding of the role of AI in education, particularly in the context of chatbots for academic guidance, online learning systems, and automated assessments, is key to enhancing the quality of education and preparing students for a technology-driven future.

Artificial intelligence (AI) and its impact on society have received a great deal of attention in the past five years since the first Stanford AI100 report. AI already globally impacts individuals in critical and personal ways, and many industries will continue to experience disruptions as the full algorithmic effects are understood. However, with regard to education, adopting in disciplines remains limited largely to Computer Science and Information Technology in postsecondary education. Recent advances with technology are especially promising for their potential to create and scale personalized learning for students, to optimize strategies for learning outcomes, and to increase access to a more diverse population. Research has confirmed that the current use of AI in education (AIEd) leads to positive outcomes, including improved learning outcomes for students, along with increased access, increased retention, lower cost of education, and decreased time to completion. Future uses of AI will include the following: enabling engaging and interactive education anytime and anywhere; personalized AI mentors that will help students identify and reach their goals; and mass-personalization that will allow AI to be tailored to each student’s learning style, level, and needs. Yet with all the potential benefits that AI and machine learning (ML) may provide students, there remains a general reticence to adopt this technology because of misconceptions and perceptions that elementary educators will need expensive equipment, robust support from IT, or to retool and learn programming or coding. As such, this study seeks to identify current perceptions early childhood, and elementary educators in the state of Missouri, USA have towards AI in general; the policies, training and existing resources in districts regarding technology in general and AI in particular; relative comfort with technology and willingness of educators to adopt new technologies for classroom instruction; and perform a needs assessment for necessary infrastructure, including reliable internet access, hardware and software. Results indicate a broad acceptance and willingness to adopt AI in daily activities and classroom instruction, but barriers to entry were identified as lack of resources and training.

In computer science, reinforcement learning is a powerful framework with which artificial agents can learn to maximize their performance for any given Markov decision process (MDP). Advances over the last decade, in combination with deep neural networks, have enjoyed performance advantages over humans in many difficult task settings. However, such frameworks perform far less favorably when evaluated in their ability to generalize or transfer representations across different tasks. Existing algorithms that facilitate transfer typically are limited to cases in which the transition function or the optimal policy is portable to new contexts, but achieving “deep transfer” characteristic of human behavior has been elusive. Such transfer typically requires discovery of abstractions that permit analogical reuse of previously learned representations to superficially distinct tasks. Here, we demonstrate that abstractions that minimize error in predictions of reward outcomes generalize across tasks with different transition and reward functions. Such reward-predictive representations compress the state space of a task into a lower dimensional representation by combining states that are equivalent in terms of both the transition and reward functions. Because only state equivalences are considered, the resulting state representation is not tied to the transition and reward functions themselves and thus generalizes across tasks with different reward and transition functions. These results contrast with those using abstractions that myopically maximize reward in any given MDP and motivate further experiments in humans and animals to investigate if neural and cognitive systems involved in state representation perform abstractions that facilitate such equivalence relations.

Aim: This study aimed to assess junior high school students' attitudes toward artificial intelligence (AI) and their perceived usefulness of AI in science learning.Specifically, it sought to identify the most commonly used AI tools in science learning, determine students' attitudes toward AI, evaluate their perceived usefulness of AI, examine students' academic performance in science, and analyze the relationships among these variables. Methodology:The study employed a descriptive-correlational research design involving 246 junior high school students from Dupax Del Sur National High School.Data were collected using a self-developed survey questionnaire that was reviewed and validated by four expert science teachers.Statistical analyses included frequency and percentage, mean and standard deviation, and Pearson's r to determine relationships among variables.Results: Findings revealed that ChatGPT and Cici were the most commonly used AI tools in science learning.Students demonstrated a generally neutral attitude toward AI, while their perceived usefulness of AI in science learning was rated as useful.Students' academic performance in science was at a Very Satisfactory level.A significant relationship was found between students' attitudes toward AI and their perceived usefulness of AI in science learning.Moreover, students' attitudes toward AI showed a very weak but statistically significant negative relationship with academic performance, while perceived usefulness was not significantly related to academic performance. Conclusion:The study concludes that more positive attitudes toward AI are associated with higher perceived usefulness; however, positive attitudes alone may lead to overreliance on AI when not properly guided.Awareness of AI's benefits is insufficient to improve academic performance without structured and responsible use.These findings underscore the need for guided, ethical, and pedagogically sound AI training programs to support effective integration of AI in science learning.