How AI is rewiring the human brain: the generational transformation of cognition and knowing

This research explores the relevance and scientific novelty of conceptual features in the design of effective creative learning environments in the sphere of foreign philology. It emphasizes the often-overlooked extended intermediate phase of learning, proposing an advanced knowledge acquisition stage situated between initial creative learning and domain expertise. The article examines the interactive effects of contextual elements in various creative computer-based and AL learning environments while studying English language and addresses learning deficiencies associated with them. It underscores the importance of aligning learning and instruction with contextual factors. The relevance of this research is determined by its exploration of innovative practices, including technology integration, project-based learning, and AI applications in foreign philology. By addressing deficiencies in advanced knowledge acquisition, the research aims to guide philology and other specialists in adapting to 21st-century challenges in education and fostering inclusive creative and impactful learning environments. The scientific novelty of the research consists in its contribution into the educational field by proposing an advanced knowledge acquisition stage. It explores the dynamic interplay of contextual creative elements in computer-based learning, AI and hybrid educational environments while studying English language for special purposes, addressing learning failures and emphasizing the need for alignment with instructional design. This article aims to explore the structure of creative teaching methods and assessment strategies employed into the studying process of Professional English; to investigate and emphasize the critical relevance and scientific novelty of conceptual features in designing effective learning environments. It explores the often-overlooked extended intermediate phase of creative learning and introduces the concept of an advanced knowledge acquisition stage. The research delves into the interactive effects of contextual elements in various computer-based learning environments and identifies learning deficiencies associated with them. Furthermore, the article upholders for the alignment of learning and instruction with contextual factors. The methodology involves a comprehensive literature review on cognitive differences, contextual elements, and recent innovations in education. The ultimate goal is to contribute valuable insights to guide educational institutions in adapting to the 21st-century landscape and fostering inclusive and impactful creative learning environments. The purpose of this article is to investigate and underscore the critical relevance and scientific novelty of creative of using computer-based learning and Artificial intelligence in educational process, partially in studying Professional English, associated with conceptual features in the design of effective creative learning environments. This research aims to bring attention to the often-neglected extended intermediate phase of creative teaching and learning Professional English and introduce the concept of an advanced knowledge acquisition stage. Through an exploration of the interactive effects of contextual elements in various computer-based and AI creative learning environments, the article identify patterns of learning deficiencies. The methodology involves a comprehensive literature review focusing on cognitive differences, contextual elements, and recent innovations in education. Ultimately, the article provides valuable insights that reflects an experience of adapting to the teaching and learning Professional English in the epoch of AI. The methodology involves a comprehensive review of literature on cognitive differences between novice and expert learners, the interplay of contextual elements in learning environments, and recent innovations in education. The research also incorporates an analysis of patterns of deficiency in advanced knowledge acquisition and explores various strategies for incorporating innovations into university education.

1. Teaching Situations and Physics Knowledge.- Introductory University Courses and Open Environment Approaches: The Computer as a Multi-role Mediator in Teaching/Learning Physics.- Practical Work Aid: Knowledge Representation in a Model Based AI System.- Simultaneous Processing of Different Problem Aspects in Expert Problem Solving: An Analysis in the Domain of Physics on the Basis of Formal Theories of Commonsense Knowledge.- Modelis: An Artificial Intelligence System Which Models Thermodynamics Textbook Problems.- 2. Different Approaches to Student Modelling.- Steps Towards the Formalisation of a Psycho-logic of Motion.- Computerized Analysis of Students' Ability to Process Information in the Area of Basic Electricity.- Modelling the Learner: Lessons from the Study of Knowledge Reorganization in Astronomy.- Cognitive Theories as a Basis for Student Modelling.- Computer-Based Learning Environment and Automatic Diagnostic System for Superimposition of Motion.- Eliciting Hypothesis-Driven Learning in a Computer-Based Discovery Environment.- 3. The Interaction Learner/Learning Environment.- An Analysis of Cooperation and Conflict in Students' Collaborative Explanations for Phenomena in Mechanics.- Analysis of Interfaces from the Point of View of Epistemology and Didactics.- 4. Design of Learning Environments.- Computer Simulation of Historical Experiments and Understanding of Physics Concepts.- The Design of a Learning Environment in Mechanics: Two Case Studies.- From Research in Science Education to the Conception of an ITS.- Research as a Guide for the Design of Intelligent Learning Environments.- Enhancing and Evaluating Students' Learning of Motion Concepts.- List of Authors.

Instructional designers use learning analytics information to evaluate designs of learning environments, learning materials, and tasks, and adjust difficulty levels, as well as measure the impact of interventions and feedback. Integrating real-time educational data and analysis into the design of learning environments, referred to as learning analytics design (LAD), seems to be a promising approach. Valid pedagogical recommendations may be suggested on the fly as learning analytics methodologies and visualizations evolve and as reliable tools become available and ready for classroom practice. This chapter aims to offer an overview on design and analytics of learning environments before reviewing opportunities of learning analytics design for optimizing learning environments in near real time. Learning analytics (LA) use static and dynamic information about learners and learning environments—assessing, eliciting, and analyzing them—for real-time modeling, prediction, and optimization of learning processes, learning environments, and educational decision-making.

<p>Learning environments are important spaces because these are where primary school children spend many hours. These environments can vary from single cell classrooms to modern open plan learning studios. As the design of these learning environments can affect the learning outcomes of students, their design and the design process behind them are important fields of investigation. Involving the users of learning environments in the design process is an important factor to be considered. Studies overseas stress the importance of involving teachers and students in the design process of learning environments. However, studies about learning environments in New Zealand show less consideration for the internal layout of classrooms and the involvement of users in their design process. Thus, this thesis studies and compares the design process behind learning environments in New Zealand with those overseas and the effect of this involvement on the design of primary school internal learning spaces, specifically classrooms. The aim of this thesis is create an understanding of the design process behind primary school classroom learning environments in New Zealand. To achieve the aim, this thesis undertakes five phases of study. The first phase is surveying primary school teachers and architects who design educational spaces, about the design and design process of learning environments in New Zealand. The survey results show that both teachers and architects support participatory design in schools and wish for more student user involvement. The second phase is a trial using social media to encourage more teacher and student participation in designing learning environments. Wordpress and Facebook groups were used for this experiment and teachers and students of primary schools in New Zealand were invited to participate. The trial result appears to indicate that social media does not work in encouraging students and teachers in thinking about the design of learning environments in general without having a specific project as a focus. The third phase is a workshop gathering five teachers and one architect to discuss the detail of the design process behind learning environments in New Zealand. The workshop result suggests that again participants support participatory design but suggest the need for guidance on how to do this, possibly from the Ministry of Education. The fourth phase is a case study of the early stages of a re‐build project for Thorndon Primary School in Wellington city. The case study included interviews, focus groups, observations, and collecting documentation. The main conclusion from the case study is that all parties to the project were in support of participatory design but would have benefitted from guidance as the whole design process and user involvement in it is unclear. The last phase is also case studies but here the focus is on the design process for rearranging the internal layout of two classrooms in two primary schools in Wellington city. The case studies covered observing the involvement of students in the design process, some classroom and brainstorming sessions, and interviews with teachers. The main result of this phase is the observation that students enjoy working on the design of their own environments and that they are able and ready to work as part of such a design process. The key conclusions of this thesis are that all parties involved in this research supported user participation in the design process, but in all the cases investigated there is almost no proper participatory design; students enjoy designing their learning environments and that enjoyment makes them belong and connect to these more; and proper preliminary guidelines for participatory design in learning environments could improve and encourage user involvement in designing learning environments in New Zealand.</p>

<p>Learning environments are important spaces because these are where primary school children spend many hours. These environments can vary from single cell classrooms to modern open plan learning studios. As the design of these learning environments can affect the learning outcomes of students, their design and the design process behind them are important fields of investigation. Involving the users of learning environments in the design process is an important factor to be considered. Studies overseas stress the importance of involving teachers and students in the design process of learning environments. However, studies about learning environments in New Zealand show less consideration for the internal layout of classrooms and the involvement of users in their design process. Thus, this thesis studies and compares the design process behind learning environments in New Zealand with those overseas and the effect of this involvement on the design of primary school internal learning spaces, specifically classrooms. The aim of this thesis is create an understanding of the design process behind primary school classroom learning environments in New Zealand. To achieve the aim, this thesis undertakes five phases of study. The first phase is surveying primary school teachers and architects who design educational spaces, about the design and design process of learning environments in New Zealand. The survey results show that both teachers and architects support participatory design in schools and wish for more student user involvement. The second phase is a trial using social media to encourage more teacher and student participation in designing learning environments. Wordpress and Facebook groups were used for this experiment and teachers and students of primary schools in New Zealand were invited to participate. The trial result appears to indicate that social media does not work in encouraging students and teachers in thinking about the design of learning environments in general without having a specific project as a focus. The third phase is a workshop gathering five teachers and one architect to discuss the detail of the design process behind learning environments in New Zealand. The workshop result suggests that again participants support participatory design but suggest the need for guidance on how to do this, possibly from the Ministry of Education. The fourth phase is a case study of the early stages of a re‐build project for Thorndon Primary School in Wellington city. The case study included interviews, focus groups, observations, and collecting documentation. The main conclusion from the case study is that all parties to the project were in support of participatory design but would have benefitted from guidance as the whole design process and user involvement in it is unclear. The last phase is also case studies but here the focus is on the design process for rearranging the internal layout of two classrooms in two primary schools in Wellington city. The case studies covered observing the involvement of students in the design process, some classroom and brainstorming sessions, and interviews with teachers. The main result of this phase is the observation that students enjoy working on the design of their own environments and that they are able and ready to work as part of such a design process. The key conclusions of this thesis are that all parties involved in this research supported user participation in the design process, but in all the cases investigated there is almost no proper participatory design; students enjoy designing their learning environments and that enjoyment makes them belong and connect to these more; and proper preliminary guidelines for participatory design in learning environments could improve and encourage user involvement in designing learning environments in New Zealand.</p>

Current learning technology is younger than the students learning with it. It has adopted tools and techniques from many contemporary disciplines: cognition, education, linguistics, semantics, artificial intelligence, ergonomics, computer science, and software engineering. As the tools and techniques are also recent there is precious little experience so far of actual learning results with the new technology. This book, basedon a NATO workshop held in Mierlo, The Netherlands, in November 1990, concentrates on the learner and on shaping the learning environment by elucidating learning behavior through student modeling and learning monitors. Another main topic is the design of interactive learning environments that promote learning by taking advantage of the individual student's needs and interests. Cognitive modeling and interactive environments are discussedin the framework of language learning. The book is divided into three sections, on cognitive modeling, language learning, and interactive environments, each of which opens with a discussion chapter presenting the topics in a general perspective. The book comes with two diskettes, for Apple and IBM PCs respectively, containing interactive exploratory learning programs.

Virtual worlds provide pre-service teachers with the opportunity to study teaching and learning in an immersive 3D computer based environment. The pre-service teacher is able to become a designer of learning environments in ways that were previously impossible in a traditional bricks and mortar classroom. The learning environment that pre-service teachers create can, in turn, inform established educators about the usefulness of virtual worlds for education. In the School of Education at Southern Cross University, Australia, pre-service teachers have been given the opportunity to design and build virtual world learning environments. This chapter presents the story of one pre-service teacher and her tutor as they discuss the design of a virtual world learning environment for maths. This particular design project resulted in virtual worlds being integrated across a number of pre-service teacher courses and extended into the K-6 classroom. An overview of these other projects is also presented.

The integration of artificial intelligence (AI) into educational environments is fundamentally transforming the learning process, raising new questions regarding student engagement and motivation. This empirical study investigates the relationship between AI-based learning support and the experience of flow, defined as the optimal state of deep attention and intrinsic motivation, among university students. Building on Csíkszentmihályi’s flow theory and current models of technology-enhanced learning, we applied a validated, purposefully developed AI questionnaire (AIFLQ) to 142 students from two Hungarian universities: the Ludovika University of Public Service and Eszterházy Károly Catholic University. The participants used generative AI tools (e.g., ChatGPT 4, SUNO) during their academic tasks. Based on the results of the Mann–Whitney U test, significant differences were found between students from the two universities in the immersion and balance factors, as well as in the overall flow score, while the AI-related factor showed no statistically significant differences. The sustainability of the flow experience appears to be linked more to pedagogical methodological factors than to institutional ones, highlighting the importance of instructional support in fostering optimal learning experiences. Demographic variables also influenced the flow experience. In gender comparisons, female students showed significantly higher values for the immersion factor. According to the Kruskal–Wallis test, educational attainment also affected the flow experience, with students holding higher education degrees achieving higher flow scores. Our findings suggest that through the conscious design of AI tools and learning environments, taking into account instructional support and learner characteristics, it is possible to promote the development of optimal learning states. This research provides empirical evidence at the intersection of AI and motivational psychology, contributing to both domestic and international discourse in educational psychology and digital pedagogy.

Although the integration of Artificial Intelligence (AI) in education has received growing attention, limited research has explicitly examined the development and application of AI-assisted exploratory learning media in mathematics. This study aims to analyze the characteristics, implementation, and potential effectiveness of AI-based learning tools in enhancing students’ mathematical problem-solving and critical thinking skills. A Systematic Literature Review (SLR) was conducted, drawing from studies published between 2016 and 2025 in the Google Scholar and Scopus databases. Using predefined inclusion and exclusion criteria, 20 relevant articles were selected and thematically synthesized to identify prevailing trends and research gaps. The findings reveal that AI integration in mathematics education predominantly supports personalized, adaptive, and interactive learning, fostering deeper conceptual understanding and the development of higher-order thinking skills. However, few studies have focused specifically on AI-assisted mathematics learning media. Key challenges identified include insufficient teacher preparedness, ethical concerns surrounding AI deployment, and the complexity of integrating AI tools into existing curricula. The study contributes theoretically by positioning AI as a cognitive and dialogic partner in the learning process, rather than a purely computational instrument. It also offers practical insights into the design and implementation of AI-supported learning environments. The analysis concludes that when integrated with digital tools such as EdCafe AI, GeoGebra, Scratch, and Canva, AI-assisted exploratory media can significantly enhance students’ conceptual mastery and problem-solving capabilities in mathematics. The study recommends targeted teacher training, the development of ethical frameworks for AI use, and increased empirical research to support responsible, student-centered implementation of AI in mathematics education.

Introduction Author Arthur C. Clarke famously argued that in science fiction literature “any sufficiently advanced technology is indistinguishable from magic” (Clarke). On 30 November 2022, technology company OpenAI publicly released their Large Language Model (LLM)-based chatbot ChatGPT (Chat Generative Pre-Trained Transformer), and instantly it was hailed as world-changing. Initial media stories about ChatGPT highlighted the speed with which it generated new material as evidence that this tool might be both genuinely creative and actually intelligent, in both exciting and disturbing ways. Indeed, ChatGPT is part of a larger pool of Generative Artificial Intelligence (AI) tools that can very quickly generate seemingly novel outputs in a variety of media formats based on text prompts written by users. Yet, claims that AI has become sentient, or has even reached a recognisable level of general intelligence, remain in the realm of science fiction, for now at least (Leaver). That has not stopped technology companies, scientists, and others from suggesting that super-smart AI is just around the corner. Exemplifying this, the same people creating generative AI are also vocal signatories of public letters that ostensibly call for a temporary halt in AI development, but these letters are simultaneously feeding the myth that these tools are so powerful that they are the early form of imminent super-intelligent machines. For many people, the combination of AI technologies and media hype means generative AIs are basically magical insomuch as their workings seem impenetrable, and their existence could ostensibly change the world. This article explores how the hype around ChatGPT and generative AI was deployed across the first six months of 2023, and how these technologies were positioned as either utopian or dystopian, always seemingly magical, but never banal. We look at some initial responses to generative AI, ranging from schools in Australia to picket lines in Hollywood. We offer a critique of the utopian/dystopian binary positioning of generative AI, aligning with critics who rightly argue that focussing on these extremes displaces the more grounded and immediate challenges generative AI bring that need urgent answers. Finally, we loop back to the role of schools and educators in repositioning generative AI as something to be tested, examined, scrutinised, and played with both to ground understandings of generative AI, while also preparing today’s students for a future where these tools will be part of their work and cultural landscapes. Hype, Schools, and Hollywood In December 2022, one month after OpenAI launched ChatGPT, Elon Musk tweeted: “ChatGPT is scary good. We are not far from dangerously strong AI”. Musk’s post was retweeted 9400 times, liked 73 thousand times, and presumably seen by most of his 150 million Twitter followers. This type of engagement typified the early hype and language that surrounded the launch of ChatGPT, with reports that “crypto” had been replaced by generative AI as the “hot tech topic” and hopes that it would be “‘transformative’ for business” (Browne). By March 2023, global economic analysts at Goldman Sachs had released a report on the potentially transformative effects of generative AI, saying that it marked the “brink of a rapid acceleration in task automation that will drive labor cost savings and raise productivity” (Hatzius et al.). Further, they concluded that “its ability to generate content that is indistinguishable from human-created output and to break down communication barriers between humans and machines reflects a major advancement with potentially large macroeconomic effects” (Hatzius et al.). Speculation about the potentially transformative power and reach of generative AI technology was reinforced by warnings that it could also lead to “significant disruption” of the labour market, and the potential automation of up to 300 million jobs, with associated job losses for humans (Hatzius et al.). In addition, there was widespread buzz that ChatGPT’s “rationalization process may evidence human-like cognition” (Browne), claims that were supported by the emergent language of ChatGPT. The technology was explained as being “trained” on a “corpus” of datasets, using a “neural network” capable of producing “natural language“” (Dsouza), positioning the technology as human-like, and more than ‘artificial’ intelligence. Incorrect responses or errors produced by the tech were termed “hallucinations”, akin to magical thinking, which OpenAI founder Sam Altman insisted wasn’t a word that he associated with sentience (Intelligencer staff). Indeed, Altman asserts that he rejects moves to “anthropomorphize” (Intelligencer staff) the technology; however, arguably the language, hype, and Altman’s well-publicised misgivings about ChatGPT have had the combined effect of shaping our understanding of this generative AI as alive, vast, fast-moving, and potentially lethal to humanity. Unsurprisingly, the hype around the transformative effects of ChatGPT and its ability to generate ‘human-like’ answers and sophisticated essay-style responses was matched by a concomitant panic throughout educational institutions. The beginning of the 2023 Australian school year was marked by schools and state education ministers meeting to discuss the emerging problem of ChatGPT in the education system (Hiatt). Every state in Australia, bar South Australia, banned the use of the technology in public schools, with a “national expert task force” formed to “guide” schools on how to navigate ChatGPT in the classroom (Hiatt). Globally, schools banned the technology amid fears that students could use it to generate convincing essay responses whose plagiarism would be undetectable with current software (Clarence-Smith). Some schools banned the technology citing concerns that it would have a “negative impact on student learning”, while others cited its “lack of reliable safeguards preventing these tools exposing students to potentially explicit and harmful content” (Cassidy). ChatGPT investor Musk famously tweeted, “It’s a new world. Goodbye homework!”, further fuelling the growing alarm about the freely available technology that could “churn out convincing essays which can't be detected by their existing anti-plagiarism software” (Clarence-Smith). Universities were reported to be moving towards more “in-person supervision and increased paper assessments” (SBS), rather than essay-style assessments, in a bid to out-manoeuvre ChatGPT’s plagiarism potential. Seven months on, concerns about the technology seem to have been dialled back, with educators more curious about the ways the technology can be integrated into the classroom to good effect (Liu et al.); however, the full implications and impacts of the generative AI are still emerging. In May 2023, the Writer’s Guild of America (WGA), the union representing screenwriters across the US creative industries, went on strike, and one of their core issues were “regulations on the use of artificial intelligence in writing” (Porter). Early in the negotiations, Chris Keyser, co-chair of the WGA’s negotiating committee, lamented that “no one knows exactly what AI’s going to be, but the fact that the companies won’t talk about it is the best indication we’ve had that we have a reason to fear it” (Grobar). At the same time, the Screen Actors’ Guild (SAG) warned that members were being asked to agree to contracts that stipulated that an actor’s voice could be re-used in future scenarios without that actor’s additional consent, potentially reducing actors to a dataset to be animated by generative AI technologies (Scheiber and Koblin). In a statement issued by SAG, they made their position clear that the creation or (re)animation of any digital likeness of any part of an actor must be recognised as labour and properly paid, also warning that any attempt to legislate around these rights should be strongly resisted (Screen Actors Guild). Unlike the more sensationalised hype, the WGA and SAG responses to generative AI are grounded in labour relations. These unions quite rightly fear the immediate future where human labour could be augmented, reclassified, and exploited by, and in the name of, algorithmic systems. Screenwriters, for example, might be hired at much lower pay rates to edit scripts first generated by ChatGPT, even if those editors would really be doing most of the creative work to turn something clichéd and predictable into something more appealing. Rather than a dystopian world where machines do all the work, the WGA and SAG protests railed against a world where workers would be paid less because executives could pretend generative AI was doing most of the work (Bender). The Open Letter and Promotion of AI Panic In an open letter that received enormous press and media uptake, many of the leading figures in AI called for a pause in AI development since “advanced AI could represent a profound change in the history of life on Earth”; they warned early 2023 had already seen “an out-of-control race to develop and deploy ever more powerful digital minds that no one – not even their creators – can understand, predict, or reliably control” (Future of Life Institute). Further, the open letter signatories called on “all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4”, arguing that “labs and independent experts should use this pause to jointly develop and implement a set of shared safety protocols for advanced AI design and development that are rigorously audited and overseen by independent outside experts” (Future of Life Institute). Notably, many of the signatories work for the very companies involved in the “out-of-control race”. Indeed, while this letter could be read as a moment of ethical clarity for the AI industry, a more cynical reading might just be that in warning that their AIs could effectively destroy the w

This Article entitles that, the role of Artificial Intelligence in psychology states that Psychology is the study of mental processes and behaviour of individuals. It is about artificial cognitive processes required for an artificially intelligent entity to be intelligent, learning, autonomous and self-developing. In psychology there are several specialties or focuses of study. In cognitive psychology, how the brain thinks and works. This includes learning, memory, perception, language and Logic. There is also developmental psychology that considers how an individual adapts and changes during different developmental stages and what is appropriate to consider of a human based on development. Sports psychology considers how to affect individual performance and how performance affects the individual. So Artificial Psychology for the purposes of this paper contains the artificial mental process considered necessary to create intelligent, autonomous, self-evolving, artificially cognitive systems. “Real artificial intelligence” as the simulation of actual human behaviour is often referred to as artificial general intelligence (AGI). In AGI, machines are supposed to be built in a way that makes it impossible to distinguish them from real humans. There are three areas, in which psychology needs to play a leading role:1. User Experience (UX) in the interaction with artificial intelligence. The psychological impact of AI on humans 2. Other psychological concepts, such as emotion and empathy will have to be created in the same way as intelligence for example “Artificial Empathy” to recreate a fully functioning artificial general intelligence 3. “Mental” health of both machines and human beings.

Design of appropriate learning environment has a significant importance in creation of aims of the math teaching. In the design of learning environments, teachers play a significant role. The aim of this study is determination of opinions of the math teachers concerning the learning environment that they design. In accordance with this aim, an opinion form which is comprised of open-ended questions is applied on 30 math teachers who are in charge in Middle Anatolian Region in Turkey. The data which are obtained as result of the application have been analysed and presented by using frequencies and percentages. It is understood from the obtained results that teachers benefit from the textbooks and auxiliary test books for designing the teaching environment, and they don't often give a place to different teaching methods and techniques. Article visualizations:

The use of information characteristics to design powerful learning environments has always been at the heart of cognitive load research. In order to promote understanding, the learners' resources should be allocated as much as possible to processes that contribute to schema acquisition. To rephrase this in the terminology used in cognitive load research: The learner's germane load should be optimized and their extraneous load should be minimized (Sweller, Van Merrienboer, & Paas, 1998; Van Merrienboer & Sweller, 2005). This important principle is the backbone of many studies conducted ever since the introduction of cognitive load theory (CLT; Sweller, 1988). This principle immediately provides us with two essential characteristics of a powerful learning environment. First of all, the design of the learning environment itself should be taken into account. How are the learning materials or problems presented to the learner? In what way does the learner interact with the environment? Are there elements in the environment that might be a source of extraneous cognitive load (e.g., split attention effect, redundancy effect)? Secondly, the background of the users should be taken into consideration. What do they already know? What is their motivation to use this learning environment? But also, and often forgotten, what is their age? The papers in this special issue reflect the continuing endeavour of cognitive load researchers to optimize instructional design by considering the individual characteristics of the learner at all times. Below I will discuss the studies reported in this special issue on Emerging Topics in Cognitive Load Research: Using Information and Learner Characteristics in the Design of Powerful Learning Environments. In order to examine the effectiveness of reducing task complexity (i.e., intrinsic cognitive load) Ayres (this issue) conducts two experiments in which high school students had to solve mathematical problems. His results showed that low (mathematical) ability students benefited more, in terms of error scores in the test phase, from an isolated or part- task strategy than high ability students, who benefited more from an integrated or whole- task strategy. This study is a nice example of the interaction of instructional method and ability level on performance. Kalyuga and colleagues were the first within a cognitive load framework to demonstrate that design solutions for novices do not necessarily transfer to higher ability levels and vice versa (Kalyuga, Ayres, Chandler, & Sweller, 1998).

The designated learning environment should be created as a unique learning space for autistic children and consider the sensory issues to overcome their needs. This learning environment would help Autism Spectrum Disorder (ASD) to continue their education in different environments to survive independently in the real world. This study used the variables of sensory stimulation, sensory sensitivity, sensory design, and physical learning environment to construct a questionnaire. It would distribute to architects towards achieving their level of knowledge and awareness. Findings are useful in the future for architects and designers when making decisions to provide conducive facilities for the autistic.

The design of learning environments is being increasingly investigated, largely as a result of higher-education providers being challenged by both societal and technological developments. These providers are becoming more aware that the quality of learning environments affects students’ approaches to learning and satisfaction. This paper presents an alternative to more-traditional methods for designing learning environments that is driven by input of their main stakeholders: students and teachers. By using this method, we were able to explore stakeholders’ insights into learning spaces design and how learning technologies can be integrated in such spaces. Qualitative research was conducted with the aim of guiding the redesign of technology-enhanced learning environments. For this particular research, we used ‘sandpits’, which are creative and design-thinking workshops, in which participants are encouraged to redesign provocative concepts of a large and a small technology-enhanced learning environment. Thirteen ‘sandpits’ were delivered involving 32 teachers and 25 students. Through these design-thinking workshops, students and teachers reflected on and discussed the role of technology in face-to-face learning and teaching and proposed new design solutions for technology-enhanced learning environments.