INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025  
Learning Environment, Learning Outcomes and Artificial  
Intelligence: The Synergistic Relationship  
Joanne Nabwire Lyanda  
Department of Curriculum and Instructional Technology, Masinde Muliro University of Science and  
Technology, Kenya  
DOI: https://dx.doi.org/10.47772/IJRISS.2025.91100510  
Received: 28 November 2025; Accepted: 04 December 2025; Published: 20 December 2025  
ABSTRACT  
Artificial intelligence (AI) is reshaping the educational landscape by turning traditional classrooms into  
interactive, flexible, and personalised learning settings. This meta-analysis examines the incorporation of AI in  
education, highlighting its impact on learning environment and learning outcomes. It examines how AI  
technologies, via personalized learning pathways, smart tutoring systems, and immediate feedback, assist  
educators in delivering instruction suited to students’ unique needs, preferences, and learning styles. Using  
environmental and pedagogical frameworks, the study explores the diverse effects of AI on essential aspects of  
the learning environment such as cognitive, emotional, physical, psychosocial and educational aspects. It shows  
that AI-powered tools enhance engagement and motivation while fostering equity and inclusion by adapting to  
different learner abilities. The study also shows how AI facilitates the simplification of administrative  
responsibilities like grading and data management, allowing teachers to focus on more significant student  
interactions throughout the learning process. There is a significant relationship between AI-powered learning  
settings and better educational results, underpinned by theoretical perspectives from Bandura’s social cognitive  
theory. As students’ behaviours and interactions with AI change, so do the results of their educational  
experiences. Novelty and innovations are essential for sustaining learner engagement when utilizing AI. The use  
of AI in education enables a transition from content-focused teaching to student-centred learning, resulting in  
more engaging, efficient and inclusive educational experiences. Educators are advised to adopt new technologies  
such as AI, AR and VR to address the requirements of 21st century students and enhance learning outcomes in  
an evolving learning environment.  
Keywords: Learning environment, Artificial Intelligence, Learning outcomes  
INTRODUCTION  
Artificial intelligence (AI) has developed into a significant and transformative power that impacts numerous  
sectors and changes human habits and business functions. AI has the potential to transform traditional education  
and create a new era of personalized learning experiences (Using AI in Education to Help Teachers and Their  
Students, 2025). This study focuses on how AI is used in education and its significant role in enhancing learning  
experiences and improving outcomes, contrasting with the traditional focus on syllabus coverage in many  
schools. This discussion aims to illustrate how AI has been used to improve education, especially in improving  
students' learning experiences over time (Xu, 2024).  
The learning environment and learning outcomes are fundamental concepts in educational research. A supportive  
learning environment, whether physical, psychological, or technological, fosters conditions where learners can  
thrive (Fraser, 2015). Learning outcomes represent the cognitive, affective, and skill-based changes that occur  
due to educational experiences (Biggs & Tang, 2011). With the rapid rise of AI in education, researchers are  
increasingly viewing AI as a powerful force that can reshape both learning environment and outcomes (Luckin  
et al., 2016).  
Using AI, teachers can gain valuable insights into students' learning habits, preferences, and strengths, allowing  
them to customise instruction to meet individual needs. Personalised learning, made possible by AI, ensures that  
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students receive content and learning experiences tailored to their own learning styles, abilities, and interests.  
This approach encourages greater engagement with material, enhancing students’ motivation and enthusiasm for  
learning.  
AI is being integrated into education for various purposes. First, it aims to improve outcomes by offering  
personalized learning paths that adjust according to students' progress and understanding. AI systems continually  
assess students' knowledge and skills, allowing educators to identify areas needing more support for focused  
interventions. Second, AI improves teaching efficiency by automating administrative activities, such as grading  
and data management, so teachers can focus on building meaningful interactions with students. Third, AI  
supports inclusive learning by adapting to different learning speeds and abilities, ensuring that all students can  
succeed. Furthermore, AI promotes lifelong learning by offering personalized recommendations for ongoing  
skill development. By incorporating AI into education, students can take charge of their learning experiences  
while allowing teachers to guide their knowledge.  
As AI technologies advance and become more sophisticated, their potential to revolutionize education continues  
to grow. Consequently, AI in education can change teaching methods and learning processes. The shift toward  
personalized learning experiences, facilitated by AI, is likely to increase student engagement, motivation, and  
academic performance. With AI technology, educational institutions can create adaptable, efficient, and  
inclusive learning environments that maximize each student's potential, transforming the pursuit of knowledge  
into an empowering experience (Deri, et al 2024).  
Learning environment  
The learning environment is where students feel motivated and comfortable to learn. A stimulus field consists  
of triggers that evoke responses from learners, facilitating learning and possibly resulting in changes in behavior;  
the field refers to the surroundings that accommodate these triggers. Stimuli are things in the environment that  
can provoke responses and affect behavior. The learning process involves learners reacting to stimuli within this  
field. Learning theorists focus on the learner and their environment, emphasizing the relationship between the  
learner and the stimuli they respond to (Olson & Ramírez, 2020).  
A safe educational setting is crucial for students' intellectual and personal growth (Sayfulloevna, 2023). When  
creating a learning environment, it is essential to consider all factors that affect student development. The  
physical, cognitive, and emotional aspects should all be taken into account (Thompson & Wheeler, 2010).  
Educators have the responsibility to nurture an atmosphere where learners feel secure and empowered to explore  
and innovate.  
When a learning environment does not meet students' needs, it creates discomfort for both students and teachers  
and fails to support learning (Thompson & Wheeler, 2010). Teaching and learning have often been separated  
from the physical space, but physical factors significantly affect learning outcomes (Guney & Al, 2012).  
Therefore, the importance of the learning environment on students' academic performance cannot be overstated.  
Research (Cayubit, (2022); Li, & Xue, (2023); Rusticus, et al (2023); Nguyen, et al (2022); Cheung, et al (2021)  
has shown that various characteristics of the learning environment impact the educational experience. These  
include:  
Intellectual dimension  
This aspect of a learning environment emphasizes deep understanding, critical thinking, and active knowledge  
creation. It encourages students to engage in advanced cognitive processes and effective communication. The  
intellectual dimension values understanding essential concepts and skills rather than mere memorization.  
Students are encouraged to express their thoughts, participate in discussions, and collaborate with peers to  
enhance comprehension. AI supports cognitive development with personalized learning, adaptive content,  
immediate feedback, and intelligent tutoring. It fosters higher-order thinking, creativity, and self-reflection by  
reducing routine tasks and providing data-driven insights.  
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Physical dimension  
The physical aspect of a learning environment encompasses structural elements such as technology, equipment,  
and furniture (Hannafin and Land 1997). The classroom's physical setup and available resources can either  
support or hinder various teaching strategies (Beckers 2019; Marmot 2014). Research indicates that color,  
texture, vistas, light, acoustics, temperature, and air quality are crucial components of the physical learning  
environment (Marmot 2014), although aesthetic factors are seen as less significant (Beckers 2019). AI  
transforms physical learning spaces into tech-enabled environments with smart classrooms, digital devices,  
VR/AR, and remote learning tools. It also raises ergonomic concerns, particularly with increased screen time.  
Affective dimension  
The emotional dimension includes students' attitudes and perceptions. Their beliefs and feelings about learning  
and their abilities greatly affect the learning process. Motivation, which refers to the drive and excitement to  
engage in education, is another key component. A student's self-efficacy, or their confidence in being successful  
in educational tasks, also plays a role. Without self-belief, students may struggle in their learning efforts.  
Additionally, learning styles and preferences matter; every learner is unique and requires tailored instruction to  
meet their needs. The best ways individuals learn e.g visual, auditory, or kinesthetic are crucial considerations  
for the learning environment. AI influences emotions and motivation through personalized feedback, emotional  
recognition tools, and supportive systems. However, an over-reliance on AI may diminish emotional connections  
and lead to feelings of isolation.  
Pedagogical dimension  
The pedagogical aspect of the learning environment (Skordi and Fraser 2019) pertains to the activities, tools,  
resources, methods, tactics, and frameworks employed by the instructor to facilitate student learning (Hannafin  
and Land 1997). A quality learning space provides students with an optimal environment for social relations,  
collaborative work and participation, thus fostering innovation and incorporating active methodologies (Poyato  
et al, 2024). AI reshapes teaching methods by automating assessments, supporting individualized instruction,  
generating learning materials, and shifting the teacher’s role from content deliverer to facilitator and data  
interpreter.  
Psychosocial dimension  
This dimension addresses the origins of human behaviour. Personalization, involvement, student cohesion,  
contentment, task orientation, innovation, individualization, investigation, cooperation, equity, and teacher  
support are some of the aspects that define psychosocial settings. AI influences social dynamics via facilitating  
collaborative tools, functioning as a social actor, fostering inclusivity, and combating bullying. However, if  
access is unequal, it may limit human-to-human interaction while widening digital differences.  
AI's impact on the learning environment is complex and significant. It fosters intellectual growth through  
personalization, expands physical and virtual learning settings, alters emotional experiences, transforms  
pedagogical approaches, and reimagines social interactions in educational communities. However, these benefits  
come with risks, which must be carefully handled to ensure that AI enriches rather than disrupts the entire  
learning environment. AI and other technology radically altered the educational landscape, gradually displacing  
traditional learning and teaching frameworks.  
Learning and Artificial intelligence  
Artificial intelligence refers to the capacity of a computer or computer-operated robot to execute tasks typically  
linked to human cognitive processes, including reasoning. While no AIs now exhibit complete human flexibility  
across diverse domains or in activities necessitating extensive everyday knowledge, certain AIs execute certain  
tasks comparably to humans.  
AI personalizes learning experiences for students, tailoring to their specific needs and abilities while offering  
real-time feedback on their progress. In contrast, virtual and augmented reality (VR/AR) facilitate immersive  
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learning experiences, enabling students to explore and engage with virtual environments and simulations. (Alam,  
2023).  
AI-driven adaptive learning systems have revolutionized education by providing personalized learning  
experiences customized to specific student requirements, hence improving engagement and outcomes. A study  
by Sari et al. (2024) emphasized the capacity of AI-driven systems to improve educational quality and equity. It  
delineates challenges, such as institutional technical preparedness, educator training, and infrastructural  
requirements, which are essential for effective implementation.  
AI affects learning through enhancing educational resources in the following ways:  
Providing customized Feedback: AI-driven insights empower educators to provide prompt,  
individualized responses, facilitating student learning from errors while enabling teachers to concentrate  
on more profound coaching.  
Augmenting Engagement: AI-driven tools facilitate educators in developing more dynamic and  
immersive educational experiences while maintaining the fundamental student-teacher relationship.  
Optimizing Administrative Functions: Through the automation of grading and standard tests, AI enables  
educators to allocate increased time to student interaction, mentorship, and the enhancement of teaching  
methodologies (Ghamrawi et al., 2024).  
Deliver Customized Feedback: Prompt, individualized comments facilitate students' comprehension of  
their errors.  
Augment Engagement: Interactive AI tutors and chatbots can replicate individualized tutoring  
experiences.  
Optimize Administrative Duties: Automating grading and assessments allows instructors to allocate  
more time to instruction.  
Learning environment and Artificial Intelligence  
Students' evaluations of their learning environment are a more significant predictor of learning outcomes than  
previous academic achievement (Lizzio et al., 2002). A study in Pakistan indicated that the learning environment  
consists of teaching for comprehension, evaluation, teacher-student contact, curricular consistency, active  
learning, and peer relationships. The study indicated the necessity of designing teacher training programs to  
enhance pedagogical methods and evaluation methodologies. This will foster an active learning environment  
that is conducive to achieving optimal learning outcomes for students (Raza, 2019). Vermeulen and Schmidt  
(2008 found that a conducive learning environment enhances student motivation, thereby improving learning.  
They contend that the learning environment is crucial for students' learning outcome.  
AI can transform educational settings by personalizing training, providing timely feedback, and meeting a wide  
range of learning needs. This promotes a more engaged and inclusive learning experience. Technologies are  
radically changing human cognitive processes, instructional approaches, and activities in unforeseen ways  
(Collins & Halverson, 2010). Conventional Information and Communication Technology (ICT) tools that  
enhance learning environments, including projectors, digital whiteboards, and digital textbooks, are augmented  
by an array of interactive educational technologies, such as games, robots, virtual reality (VR), computer  
simulations, block-based programming, and the Internet of Things (Weng & Chiu, 2023).  
Generative AI (GenAI) is a permanent fixture, representing only the inception of advancements in new  
technology. The inquiry has shifted from "Should we grant students access to AI?" to “How should students  
engage with AI?” (NCEE, 2024). Student experiences must be organized to ensure that learning is real, pertinent,  
and significant, utilizing the latest sophisticated technology (Framework for AI-Powered Learning  
Environments. Pdf, n.d.). Research by Jafari (2024) indicated that AI exhibited considerable potential in  
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comprehending emotions and sentiments within an educational context while Elimadi et al. (2024) found that  
AI has the capacity to enhance student learning outcomes via personalized and adaptable learning experiences.  
Learning outcomes and Artificial Intelligence  
Learning outcomes delineate the particular knowledge, abilities, or competencies that a learner will acquire from  
an educational activity. A learning outcome is a capability that students have acquired that they did not possess  
before. It constitutes a transformation in individuals stemming from an educational experience (Maher, 2004).  
Learning activities occur inside the learning environment. Learning outcomes are integral to assessment and  
evaluation, articulating the knowledge that learners are expected to acquire upon the completion of the  
educational activity. An effectively articulated learning outcome would emphasize the learner's capacity to apply  
acquired knowledge in practical scenarios, rather than merely reciting facts.  
AI influences learning outcomes since learners’ interactions in an AI enhanced environment affect their learning  
and consequently, the learning outcome. Learning outcomes are progressively being shaped by AI tools which  
are increasingly accommodating learners’ diverse needs. A study by Vieriu and Petrea (2025) in Bucharest  
demonstrated that AI provides substantial advantages, such as personalized learning, greater academic  
performance, and increased student engagement. This indicates that of AI has advanced to influence learning  
outcomes. Utilizing AI as an auxiliary resource, educators can improve educational outcomes while preserving  
their essential function in fostering student development (AI Impact on Education, n.d.) (Kim et al., 2022).  
AI chatbots significantly influence students' learning outcomes (Wu & Yu, 2024). They exert a more significant  
impact on learners in higher education than on those in basic and secondary education. Moreover, brief  
interventions demonstrated a more significant impact on students' learning outcomes compared to extended  
interventions. The novelty effects of AI chatbots may enhance learning results during brief interventions, but  
these advantages diminish with prolonged engagements. Employing chatbots can greatly enhance student  
learning experiences by allowing them to study at their own speed with less stress, saving them time, and keeping  
them motivated (Ait Baha et al, 2024). Future designers and educators ought to enhance students' learning results  
by integrating AI chatbots with anthropomorphic avatars, gamification features, and emotional intelligence.  
The integration of information technology with education and pedagogy is intensifying in the realm of  
educational informatization. Advanced information technologies, particularly AI, have effectively enhanced the  
teaching process and elevated educational quality (Xie, 2023). Education has been transformed by AI through  
the customization of learners’ experiences to meet their individual needs. This augmentation of learning through  
engagement leads to positive learning outcomes (Pratama et al., 2023). Personalised learning, a paramount  
benefit of AI in education, facilitates improved student outcomes by allowing learners to progress at their own  
pace and in alignment with their preferred learning styles. Intelligent tutoring systems, chatbots, and automated  
grading and assessment enhance efficiency, conserve educators' time, and deliver more precise and consistent  
feedback.  
The relationship between AI and learning outcomes is complex and multidimensional, encompassing not only  
academic performance but also motivational, engagement, and equity-related outcomes. One of the strongest  
relationships between AI and learning outcomes stems from AI’s ability to deliver personalized and adaptive  
learning. Traditional classrooms often apply a “one-size-fits-all” approach. Here, the same pace, content, and  
feedback is applied across board which may not suit all learners. AI can help bridge that gap by individualising  
instruction to suit each learner’s needs. AI-driven systems such as intelligent tutoring systems (ITS), adaptive  
learning platforms, and learning analytics systems tailor content, pace, and feedback based on a student’s  
strengths, weaknesses, and progress (Merino-Campos, 2025). This personalization often results in higher  
academic performance, improved knowledge retention, and increased satisfaction. Baillifard, (2023) conducted  
a case study of a “personal AI tutor” used with university students. The study revealed that those who actively  
engaged with the AI tutor achieved significantly higher exam grades compared to those who did not. Thus, by  
offering individually tailored learning experiences AI helps optimize learning for many students.  
Beyond academic performance, AI’s influence extends to motivation, engagement, confidence, and self-  
efficacy; all of which indirectly support better learning outcomes. A study by Xu (2025) found that AI-driven  
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personalized feedback significantly predicted students’ goal achievement, self-efficacy, and learning  
engagement. Through timely, adaptive feedback, AI helps learners feel more in control and confident in their  
learning journey. A study by Keong (2025) reported increased motivation when AI driven facilities are used.  
Additionally, Li (2025) found that AI enabled STEM education increased learning outcomes among learners.  
Therefore, AI helps not just by giving content, but by shaping the learner’s experiences which in turn supports  
sustained learning.  
AI affects the structural and operational aspects of education, freeing up resources and providing prompt data-  
driven support, resulting in better learning results. AI minimizes teacher workload by automating administrative  
chores such as grading and routine evaluations (Tapalova, 2022). Furthermore, AI-powered learning analytics  
can detect at-risk pupils early. This enables targeted interventions before risks become established, resulting in  
undesirable consequences like as dropouts or poor academic performance (Merino-Campos, 2025). Because of  
these structural benefits, AI can scale excellent teaching and support to many more learners, maximizing  
resource utilization while promoting superior learning outcomes.  
Inequalities exist in learning outcomes for learners left behind in the traditional classrooms. In such scenarios,  
AI provides adaptive and individualized approaches to solve such challenges. A study by Hao (2025) of an AI  
learning environment found that students who used co-constructive interactions with AI showed higher learning  
gains and motivation than their peers (Hao, 2025). Another study revealed that AI can support differentiated  
instruction thereby helping learners with diverse backgrounds, learning styles, or initial competencies (Hariyanto  
et al, 2025). Additionally, teachers and students perceive AI as helpful in identifying individual needs and  
customizing learning (A Alomair, 2024). This helps narrow achievement gaps between stronger and weaker  
students. Thus, when thoughtfully implemented, AI has potential to offer tailored support for learners who might  
otherwise struggle in standardized systems.  
While the relationship between AI and positive learning outcomes is strong and promising, it is not automatic  
or guaranteed. Several challenges can moderate or undermine this relationship. Challenges related to limited  
access and infrastructure lead to low adoption of AI tools (Matere, 2024). Teachers also need training to enable  
AI integration into the curriculum. Without proper teacher training, AI tools risk being under-utilized or misused.  
Merino-Campos (2025) observed that ethical and privacy concerns, algorithmic bias, and data security can be a  
challenge to using AI in education. He adds that the use of student data for AI-driven personalization raises  
legitimate privacy and fairness concerns. To realize the benefits of AI in education, stakeholders must address  
infrastructural, professional, ethical, and research-quality challenges. Through personalization, adaptive  
feedback, efficient administration, and enhanced engagement, AI can significantly improve academic  
performance, motivation, equity, and learning efficiency. However, realizing these benefits depends heavily on  
access, infrastructure, teacher capacity, ethical implementation, and rigorous pedagogical integration. As AI  
becomes more embedded in educational systems worldwide, stakeholders must approach adoption thoughtfully,  
balancing enthusiasm with responsibility.  
The synergistic relationship  
AI transforms the learning environment by introducing personalization, adaptivity, and responsive feedback  
loops. For example, adaptive learning systems adjust content difficulty based on real-time performance data,  
creating an environment tailored to individual needs (Holmes et al., 2019). Such environments foster engagement  
and reduce cognitive overload, thereby supporting better learning outcomes.  
The effectiveness of AI tools depends heavily on the environment in which they are used. Supportive  
environments which are characterized by teacher readiness, adequate infrastructure, and positive learner  
attitudes, maximizes AI’s impact (Luckin et al., 2016). However, if the environment is poorly equipped or lacks  
teacher competence, AI may not improve outcomes despite its potential.  
When AI operates within an enabling learning environment, learning outcomes improve significantly. Research  
shows that students in AI-enhanced environments demonstrate: higher academic achievement, deeper conceptual  
understanding, improved self-regulation and increased motivation and engagement (Koedinger et al., 2015;  
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Holmes et al., 2019). This improvement occurs because AI supports personalized progression, while the learning  
environment supports emotional safety and sustained engagement.  
The synergy among the three variables forms a circular process: AI reshapes and improves the learning  
environment; a strong learning environment increases the effectiveness of AI: enhanced learning outcomes  
justify further integration and refinement of AI tools; and improved AI tools further refine the learning  
environment. Thus, the relationship is not linear but self-reinforcing, where each component strengthens the  
others.  
The use of Intelligent Tutoring Systems in Collaborative Classrooms has permeated current learning  
environments. In such set ups, intelligent tutoring systems provide individualized feedback while teachers  
facilitate group discussions. The supportive environment encourages collaboration, while AI ensures  
personalized learning, leading to increased achievement and motivation (Woolf et al., 2013). Another example  
is use of AI-Supported learning analytics in higher education. In such scenarios, learning analytics platforms  
identify students at risk and support timely interventions. In environments where instructors actively use this  
data, learning outcomes which include retention and pass rates, improve significantly (Holmes et al., 2019).  
Additionally, AI can be used in inclusive learning environments by providing speech-to-text, predictive text, or  
adaptive reading support. Such create inclusive environments for students with disabilities. In such  
environments, learners demonstrate improved confidence and academic performance (Luckin et al., 2016).  
These examples show that AI alone is not responsible for improved outcomes. Rather it is the interaction between  
AI and the learning environment that produces the strongest effects. Recognizing this synergistic relationship  
improves decision-making around AI adoption in education. A synergistic interaction transpires when two  
entities provide a more significant impact collectively than they would separately. Synergistic interactions are  
founded on the co-creation of outcomes. The learning environment, learning outcomes, and AI possess a  
distinctive interrelationship. Bandura's social cognitive theory posits that an individual's behaviour, personal  
attributes, and the surrounding environment are in a continuous state of interaction. A modification in one  
element influences the results of the others (Van der Bijl, J. J., & Shortridge-Baggett, L. M., 2002). This study  
demonstrates that AI has significantly transformed the learning environment in undeniable ways. Consequently,  
the learning outcomes or the conduct of learners is destined to change.  
The figure shows the synergistic relationship between learning environment, learning outcomes and artificial  
intelligence.  
The synergistic relationship between AI, learning environment and learning outcome  
AI enhances the learning environment making it more adaptive, responsive, personalized and efficient.  
Additionally, having a supportive learning environment amplifies the effectiveness of AI by ensuring usability,  
acceptance and meaning teacher-student interaction. Improved learning outcomes reinforce the adoption and  
refinement of AI tools and leaning environments forming a continuous cycle. Moreover, the synergy between  
AI and the learning environment leads to optimal learning experiences which in turn improves learning  
outcomes.  
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CONCLUSION  
The incorporation of Artificial Intelligence (AI) in education significantly alters learning experiences by  
converting conventional classrooms into dynamic and individualized learning environments. AI technologies  
provide personalized assistance, instantaneous feedback, and customized learning pathways, resulting in  
improved student engagement, motivation, and academic achievement. The relationship between AI, the  
learning environment, and learning outcomes is deeply interconnected. AI enriches the learning environment  
through personalization and analytics, while supportive learning environments enhance the effectiveness of AI.  
Together, they create a synergy that leads to improved engagement, motivation, and academic performance.  
Understanding this three-way relationship is crucial for educators, researchers, and policymakers aiming to  
harness AI’s full potential in transforming education.  
RECOMMENDATIONS  
In a rapidly evolving educational landscape driven by technological advancements, it is imperative for educators  
to alter their perspectives on teaching and learning. The introduction of AI, AR, and VR in the education industry  
has fundamentally transformed learning environments and, consequently, learning outcomes. It is thus advisable  
for educators to adopt the novel modifications to enhance the educational experience for the digital natives that  
comprise the present generation of learners.  
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