bluuebunny/crossref_metadata_2025_split huggingface dataset:
Introduction to AI in Learning: Designing the Future
Authors: Niemi Hannele , Pea Roy D. , Lu Yu 
Published: November 2022

AbstractThe introduction chapter focuses on the main questions of the whole book AI in Learning: Designing the Future: (1) How is learning changing when human learning and machine learning are connected and what consequences does this conjunction have for education, also for working life as lifelong learning and (2) what kind of ethical issues are emerging with AI in education from the viewpoints of schools and other learning environments. The chapter first summarizes how recent AI technologies provide several options for learning and educational services and how AI is applied already in societies. In education and learning, many advanced techniques are already available, and we have tentatively promising findings. However, the accelerating pace of development of technology expands AI’s potentialities in education, so we need extensive new research about educational implementations and their effects on human learning and people’s lives. The chapter also summarizes how different chapters provide new research on AI in learning and education.
Learning Clinical Reasoning Through Gaming in Nursing Education: Future Scenarios of Game Metrics and Artificial Intelligence
Authors: Koivisto Jaana-Maija , Havola Sara , Mäkinen Henna , Haavisto Elina 
Published: November 2022

AbstractThe COVID-19 pandemic has challenged healthcare professionals’ clinical reasoning, which can have serious consequences for patients. So far, the use of artificial intelligence (AI) in nursing education has been limited. Artificial intelligence (AI) is one solution for ensuring quality decision-making in demanding clinical situations in two ways. First, AI applications can support healthcare professionals’ clinical decisions. Second, AI techniques can be used to support learning clinical reasoning (CR) in healthcare education and training. This chapter focuses on the potential of exploiting AI through game metrics in nursing education. Previously, simulation games have proven effective for learning clinical reasoning skills. However, game metrics have not been commonly utilized in nursing simulation games, although research in other disciplines has shown that game metrics are suitable for demonstrating the achievement of learning outcomes. This chapter discusses the possibilities of using game metrics to develop adaptive features for nursing simulation games. Personalization and adaptivity in simulation games can enable meaningful learning experiences and enable nursing students to achieve good CR skills for their future work in constantly challenging clinical situations.
AI-Supported Simulation-Based Learning: Learners’ Emotional Experiences and Self-Regulation in Challenging Situations
Authors: Ruokamo Heli , Kangas Marjaana , Vuojärvi Hanna , Sun Liping , Qvist Pekka 
Published: November 2022

AbstractThis study explores learners’ emotional experiences and self-regulation and how they overcome stressful situations in a simulation-based learning environment (SBLE). We also examine what kinds of situations an AI tutor could be used to facilitate simulation-based learning. We will first present the theoretical background of the research dealing with self-regulated learning, followed by positive and negative emotions and situations in simulation-based learning. The research questions of the study are as follows: (1) What kinds of emotions do learners experience in simulation-based learning situations? (2) Through what kinds of SRL operations do learners aim to overcome challenging situations during simulation-based learning? and (3) In what kinds of situations could an AI tutor be used to facilitate simulation-based learning? To collect data, a 1-week experiment was conducted in August 2021. The data was collected using online observations, video recordings, and delayed stimulated recall interviews. The interview data was analyzed through a deductive thematic analysis process. The results of the study show that learners experienced various positive and negative emotions and emotions are deeply connected with learning. We need to do more research to describe these connections in more detail. According to the results, an AI tutor can provide help for decision-making and visualizing critical points and help learners ask questions to check vital points in the system.
Training Hard Skills in Virtual Reality: Developing a Theoretical Framework for AI-Based Immersive Learning
Authors: Korhonen Tiina , Lindqvist Timo , Laine Joakim , Hakkarainen Kai 
Published: November 2022

AbstractAdvances in virtual reality (VR) technology afford creation of immersive virtual learning environments that simulate real-life learning contexts with increasing fidelity. When supported by sufficiently advanced artificial intelligence (AI)-based tutoring software, such environments may facilitate asynchronous, embodied learning approaches for learning hard, procedural skills in industrial settings – addressing timeliness, accuracy, and scalability issues common in the industry.This chapter reflects on the pedagogical setting of immersive virtual reality-based hard skills training guided by an AI tutor software agent. We examine the interfacing of traditional intelligent tutoring system (ITS) software with an immersive virtual environment. Further, we suggest the philosophies of embodied, embedded, enacted, and extended (4E) cognition as a way to fully consider learner epistemology in a virtual world and to account for and make full use of the unique opportunities afforded by the synthetic nature of the immersive virtual learning environment.To explore possibilities for improved pedagogical approaches, we project the 4E cognition approach into the abovementioned learning context and outline a theoretical framework for a VR-native AI tutor. We then propose VR-native pedagogical principles for such as framework that could inform follow-on research.
Deep Learning in Automatic Math Word Problem Solvers
Authors: Zhang Dongxiang 
Published: November 2022

AbstractThe design of an automatic solver for mathematical word problems (MWPs) dates back to the early 1960s and regained booming attention in recent years, owing to revolutionary advances in deep learning. Its objective is to parse the human-readable word problems into machine-understandable logical expressions. The problem is challenging due to the existence of a substantial semantic gap. To a certain extent, MWPs have been recognized as good test beds to evaluate the intelligence level of agents in terms of natural language understanding and automatic reasoning. The successful solving of MWPs can benefit online tutoring and constitute a milestone toward general AI. In this chapter, we present a general introduction to the technical evolution trend for MWP solvers in recent decades and pay particular attention to recent advancement with deep learning models. We also report their performances on public benchmark datasets, which can update readers’ understandings of the latest status of automatic math problem solvers.
Ethical Guidelines for Artificial Intelligence-Based Learning: A Transnational Study Between China and Finland
Authors: Wei Ge , Niemi Hannele 
Published: November 2022

AbstractArtificial intelligence (AI) plays an increasingly prevalent role in the global education system. However, there is a lack of transparency about how AI technologies work alongside certain ethical guidelines. Achieving the global benefits of AI-driven learning and education requires international dialogue on the commonplaces of ethical considerations. This paper, taking China and Finland as two contextual cases, analyzes how AI-related policies at the national level have focused on educational themes and established aims for improving the quality of learning and education. As a result, four themes for AI ethics have emerged: (1) inclusion and personalization, (2) justice and safety, (3) transparency and responsibility, and (4) autonomy and sustainability. Although both China and Finland recognize the importance of AI ethics, the differences are manifested in their policy approaches, properties, and strategies due to sociocultural variation. This chapter emphasizes the need for international and transnational dialogue from ethical perspectives to foster our reciprocal understanding of AI and the human-centered stance on education in the digitized age.
Artificial Intelligence Ethics from the Perspective of Educational Technology Companies and Schools
Authors: Kousa Päivi , Niemi Hannele 
Published: November 2022

AbstractThis chapter discusses the ethical issues and solutions that educational technology (EdTech) companies and schools consider during their daily work. As an example, two Finnish cases are provided, in which companies and schools were interviewed about the problems they have experienced. The chapter first reviews the regulations and guidelines behind ethical AI. There are a vast number of guidelines, regulations, and principles for ethical AI, but implementation guidelines for how that knowledge should be put into practices are lacking. The problem is acute because, with the quick pace of technological development, schools are in danger of being left behind without sufficient education for effectively managing their uses of AI’s possibilities and coping with its challenges. Issues related to security and trustworthiness are also a growing concern. This chapter does not solve the ethical problems experienced by companies and schools but brings new perspectives into view in how they appear in the light of ethical principles such as beneficence, non-maleficence, autonomy, justice, and explicability. The aim is not only to continue the discussion in the field but to find ways to reduce the gap between decision-makers, businesses, and schools.
Artificial Intelligence in Education as a Rawlsian Massively Multiplayer Game: A Thought Experiment on AI Ethics
Authors: Cowley Benjamin Ultan , Charles Darryl , Pfuhl Gerit , Rusanen Anna-Mari 
Published: November 2022

AbstractIn this chapter, we reflect on the deployment of artificial intelligence (AI) as a pedagogical and educational instrument and the challenges that arise to ensure transparency and fairness to staff and students . We describe a thought experiment: ‘simulation of AI in education as a massively multiplayer social online game’ (AIEd-MMOG). Here, all actors (humans, institutions, AI agents and algorithms) are required to conform to the definition of a player. Models of player behaviour that ‘understand’ the game space provide an application programming interface for typical algorithms, e.g. deep learning neural nets or reinforcement learning agents, to interact with humans and the game space. The definition of ‘player’ is a role designed to maximise protection and benefit for human players during interaction with AI. The concept of benefit maximisation is formally defined as a Rawlsian justice game, played within the AIEd-MMOG to facilitate transparency and trust of the algorithms involved, without requiring algorithm-specific technical solutions to, e.g. ‘peek inside the black box’. Our thought experiment for an AIEd-MMOG simulation suggests solutions for the well-known challenges of explainable AI and distributive justice.
Four Surveillance Technologies Creating Challenges for Education
Authors: Pea Roy D. , Biernacki Paulina , Bigman Maxwell , Boles Kelly , Coelho Raquel , Docherty Victoria , Garcia Jorge , Lin Veronica , Nguyen Judy , Pimentel Daniel , Pozos Rose , Reynante Brandon , Roy Ethan , Southerton Emily , Suzara Miroslav , Vishwanath Aditya 
Published: November 2022

Abstract“Ubiquitous AI”—embodied in cloud computing web services, coupled with sensors in phones and the physical world—is becoming infrastructural to cultural practices. It creates a surveillance society. We review the capabilities of four core surveillance technologies, all making headway into universities and PreK-12 schools: (1) location tracking, (2) facial identification, (3) automated speech recognition, and (4) social media mining. We pose primary issues educational research should investigate on cultural practices with these technologies. We interweave three priority themes: (1) how these technologies are shaping human development and learning; (2) current algorithmic biases and access inequities; and (3) the need for learners’ critical consciousness concerning their data privacy. We close with calls to action—research, policy and law, and practice.
Reflections on the Contributions and Future Scenarios in AI-Based Learning
Authors: Pea Roy D. , Lu Yu , Niemi Hannele 
Published: November 2022

AbstractThis chapter reflects on the contributions of different articles of the book from various perspectives. Seven categories provide perspectives to reflections. Four of them are connected to different levels of the educational system, others opening scenarios to research on education and learning with AI, and finally the last category is devoted to ethical challenges of AI in education and learning.
Assessing and Tracking Students’ Wellbeing Through an Automated Scoring System: School Day Wellbeing Model
Authors: Tang Xin , Upadyaya Katja , Toyama Hiroyuki , Kasanen Mika , Salmela-Aro Katariina 
Published: November 2022

AbstractThe assessment of student wellbeing has been often static and lagged behind for the intervention/diagnostic purpose. In this chapter, we aim to introduce an automated school wellbeing scoring dynamic real-time system, School Day Wellbeing Model. With Artificial Intelligence (AI)-based item sampling methods and answers scoring and reporting systems, the School Day Wellbeing Model can collect wellbeing data at low cognitive cost, track wellbeing real time at multiple levels (e.g., individual-, class-, school-level), and give immediate feedback. The model is constructed on the basis of the School Wellbeing Model, Study Demand-Resource Model, and OECD Social-Emotional Skill Model. In the book chapter, the wellbeing assessments, including AI-based assessments, are reviewed so that the strengths of the School Day Wellbeing Model are highlighted. User experiences are collected to show the utility of the model. During the COVID-19 pandemic, the need for such a model is imperatively high as students’ wellbeing has been largely dampened. As a result, the model has been appreciated by users and has served about 55,000 students so far in the globe. The future development of the model is also discussed.
Learning from Intelligent Social Agents as Social and Intellectual Mirrors
Authors: Maples Bethanie , Pea Roy D. , Markowitz David 
Published: November 2022

AbstractThe interaction capabilities of Intelligent Social Agents, made possible by advances in artificial intelligence, are eliciting deep emotional bonding with users, leading researchers to reexamine the impact and potential uses of these human-machine relationships in education. In this work, we examine how one best-in-class ISA, Replika, might be affecting users socially, emotionally, and cognitively. Our results indicate that many use Replika as a social and cognitive mirror, facilitating their learning and development this way. The confluence of new functionality, product narrative, and user life stressors make ISAs an emerging tool for not only emotional support, but also cognitive support and development.
An AI-Powered Teacher Assistant for Student Problem Behavior Diagnosis
Authors: Chen Penghe , Lu Yu 
Published: November 2022

AbstractStudent problem behavior refers to the students’ undesirable conducts and actions in schools. These behaviors severely affect students’ growth and development. In many cases, student problem behaviors are caused by the unsatisfied psychological needs, which are defined as the need deficiency. Hence, diagnosing students’ need deficiencies and regulating their problem behaviors are important educational tasks. The previous psychological studies have analyzed how distinct factors might affect student’s problem behaviors. However, the school teachers without such expertise may still encounter difficulties in applying these findings. Hence, this chapter describes the process of designing and implementing an intelligent teacher assistant, which could advise teachers and help them to handle the student problem behavior. Technically, it utilizes a task-oriented dialogue system to help identify the underlying reasons (i.e., the student need deficiency) behind their problem behaviors, and accordingly relies on a community question answering system to provide the advice. It also employs the semantic search technology to find the similar cases that have been well resolved by the experienced teachers.
Analysis and Improvement of Classroom Teaching Based on Artificial Intelligence
Authors: Sun Zhong , Yu Zi Chun , Xu Fei Yun 
Published: November 2022

AbstractCommon classroom teaching analysis, which has focused on counting and coding the teacher-student behaviors and discourse interactions, faces many difficulties as content-free, low efficiency, and small scale in analysis. To overcome these shortcomings, and foster high-quality classroom teaching, the current study proposes a blended human and Artificial Intelligence (AI) technology for classroom teaching analysis named as TESTII. It consists of five steps identifying teaching events, sequencing the pedagogies of classroom teaching structure, analyzing teacher-student interaction, interpreting teaching meaning, and providing improvement strategies for high-quality classroom teaching.
Learning Career Knowledge: Can AI Simulation and Machine Learning Improve Career Plans and Educational Expectations?
Authors: Chen I-Chien , Bradford Lydia , Schneider Barbara 
Published: November 2022

AbstractAs AI and machine learning permeates every area of life, its use to ameliorate educational inequities becomes of great interest. One important application of machine learning within education is to help students increase their alignment of career choice, educational attainment, and projected salary. Alignment theory has shown that having alignment yields higher educational attainment for students. Using the app, Init2Winit, which has students play a game which gives them points for correct alignment, this chapter explores how machine learning, in particular using a decision tree, can give insights into game use and its relation to educational expectations. This model builds a basis for the improvement of Init2Winit to increase student educational expectations through counselor interventions and how other educational applications could use machine learning for insights to improve educational outcomes. The model can decrease educational inequities by increasing educational attainment for those in underrepresented minorities.
Using Aichinger’s equation to characterize polynomial functions
Authors: Almira J. M. 
Published: November 2022

AbstractAichinger’s equation is used to give simple proofs of several well-known characterizations of polynomial functions as solutions of certain functional equations. Concretely, we use that Aichinger’s equation characterizes polynomial functions to solve, for arbitrary commutative groups, Ghurye–Olkin’s functional equation, Wilson’s functional equation, the Kakutani–Nagumo–Walsh functional equation, and a general version of Fréchet’s unmixed functional equation.
A note on strong conciseness in virtually nilpotent profinite groups
Authors: Detomi Eloisa 
Published: November 2022

AbstractA group word w is said to be strongly concise in a class $${\mathcal {C}}$$
C
of profinite groups if, for every group G in $${\mathcal {C}}$$
C
such that w takes less than $$2^{\aleph _0}$$

2

ℵ
0

values in G, the verbal subgroup w(G) is finite. In this paper, we prove that every group word is strongly concise in the class of virtually nilpotent profinite groups.
Mechanisms of colorectal liver metastasis development
Authors: Shasha Tal , Gruijs Mandy , van Egmond Marjolein 
Published: November 2022

AbstractColorectal cancer (CRC) is a leading cause of cancer-related death worldwide, largely due to the development of colorectal liver metastases (CRLM). For the establishment of CRLM, CRC cells must remodel their tumor-microenvironment (TME), avoid the immune system, invade the underlying stroma, survive the hostile environment of the circulation, extravasate into the liver, reprogram the hepatic microenvironment into a permissive pre-metastatic niche, and finally, awake from a dormant state to grow out into clinically detectable CRLM. These steps form part of the invasion-metastasis cascade that relies on reciprocal interactions between the tumor and its ever-changing microenvironment. Such interplay provides a strong rational for therapeutically targeting the TME. In fact, several TME constituents, such as VEGF, TGF-β coreceptor endoglin, and CXCR4, are already targeted in clinical trials. It is, however, of utmost importance to fully understand the complex interactions in the invasion-metastasis cascade to identify novel potential therapeutic targets and prevent the establishment of CRLM, which may ultimately greatly improve patient outcome.
Seven classes of antiviral agents
Authors: Ianevski Aleksandr , Ahmad Shahzaib , Anunnitipat Kraipit , Oksenych Valentyn , Zusinaite Eva , Tenson Tanel , Bjørås Magnar , Kainov Denis E. 
Published: November 2022

AbstractThe viral epidemics and pandemics have stimulated the development of known and the discovery of novel antiviral agents. About a hundred mono- and combination antiviral drugs have been already approved, whereas thousands are in development. Here, we briefly reviewed 7 classes of antiviral agents: neutralizing antibodies, neutralizing recombinant soluble human receptors, antiviral CRISPR/Cas systems, interferons, antiviral peptides, antiviral nucleic acid polymers, and antiviral small molecules. Interferons and some small molecules alone or in combinations possess broad-spectrum antiviral activity, which could be beneficial for treatment of emerging and re-emerging viral infections.