Assessing the Impact of Emerging Technologies on Teaching and Learning: A Review of Technology-Based Learning Environments

 Assessing the Impact of Emerging Technologies on Teaching and Learning: A Review of Technology-Based Learning Environments

¹Nyakongo Mokaya Clinton, ²Richard Ronoh

¹Department of Technology Education, University of Eldoret Kenya

²Department of Computer Science, Kibabii University, Kenya

DOI: https://dx.doi.org/10.47772/IJRISS.2024.803196S

Received: 19 June 2024; Revised: 08 July 2024; Accepted: 12 July 2024; Published: 17 August 2024

ABSTRACT

There has been a change in the education dynamics due to new and emerging technologies. This has created a brand-new exciting possibility for improving teaching and learning process. This review paper is motivated by a desire to synthesize literature relating to the effect of these technologies on education, more specifically technology-enhanced learning environments. This paper provides insights into the pedagogical and learner engagement implications of new technologies, such as artificial intelligence (AI), virtual realities (VR) or mobile learning based on research. Together, the synthesis underscores opportunities and hurdles of such technology integration in educational contexts aiding guidance for educators, researchers, and policymakers to negotiate an evolving landscape of how we learn with technology.

Keywords: Artificial Intelligence, Virtual Reality, Pedagogy, Differentiated Instruction, Technology-based learning environment.

INTRODUCTION

The rapid advancements in technology and evolving job demands are fundamentally reshaping the educational curriculum. Emerging technologies have come a long way to bring their effect in every sector of society including education where we see the dawn of a new era. A variety of new technologies, from interactive simulations and virtual classrooms to AI-based tutoring systems are revolutionizing pedagogical approaches. This has significantly altered and transformed how students engage with educational content. The dynamic convergence of advanced technologies in modern times also demands our understanding at the intersection between pedagogic content and teaching methods and its nexus with academic performance factors associated with learner engagement. Technology-based learning environments are more common, providing various tools and platforms for teaching/learning processes.  The evaluation of emerging technologies should be focused not just on how they can impact but also on nurturing adaptability and lifelong learning skill in a world where digital dynamics are ever changing. Dr. Michael Chen (2000) posits that effective assessment of emerging technologies does not look at the outputs but delves into the learning process thus understanding the dynamic relationship between technology and pedagogy is essential. Our main goal is to investigate the effects of emerging technologies on pedagogy and ultimately on learner engagement. We will now critically review these emerging technologies from their impact on education; paying particular attention to how they influence pedagogy, as well as learner engagement and academic outcomes. Part of this paper presents on the challenges and limitations of artificial intelligence adoption on pedagogy and learner engagement. A discussion on the implications for educators and policymakers will be highlighted and finally technical and accessibility issues relating to artificial intelligence adoption will be mentioned.

METHODOLOGY

This is a review that adopts a methodical perspective on gauging how emerging technologies affect teaching and learning; it does this within technology-centered educational setups. We embark on an all-inclusive search using terms like “emerging technologies in education,” “technology-based learning,” “virtual reality,” “augmented reality,” “artificial intelligence,” “adaptive learning” and “gamification” through reputable academic databases which encompass ERIC, PubMed, IEEE Xplore, and Google Scholar. Boolean operators was used to make the various searches more focused to artificial intelligence and thus yielding more relevant findings. The findings were enhanced by use of truncation. Inclusion criteria was handy in identifying studies conducted between 2010 and 2024. Exclusion criteria was deployed in that any research work falling short of this description or failing to provide adequate data related to educational outcomes they do not form part of our scope. Data is extracted using a pre-designed form that helps in gathering critical details such as the aims of the study, procedures followed during research and outputs.

The quality of the studies relating to artificial intelligence adoption in pedagogy and learner engagement was assessed to ensure methodological rigor. Thematic analyses was used to find patterns and themes regarding to the study. Through the findings of the research many educators, relevant stakeholders and policymakers will stand guided.

A. Impact of Emerging Technologies on Pedagogy

The evolution of pedagogy is not only evident but has imparted life positively. The pedagogy that is an art and science of its own has evolved gradually across a long stretch of time. From the era of ancient Greek philosophers to modern day education scholars, the constant quest for better and fair ways to promote learning has been greatly influenced by major developments in other fields such as artificial intelligent systems which have recently developed.

Pedagogical Principles and AI Applications

Alexander, R. (2008) emphasizes the distinction between teaching and pedagogy, stating that “teaching is an act while pedagogy is both act and discourse. Thus change in pedagogy will directly affect the way the curriculum is delivered. Governments seek to limit teachers’ activity by demanding conformity to chosen pedagogies (Smith, K. 2012). Artificial intelligence integration in education should thus be based on relevant pedagogical principles and it should integrate with existing practices for the benefit of the learner while simplifying the work of the teacher.

a. Differentiated Instruction:

Learners differ in ability and capabilities. They have also been brought up in different backgrounds and role models. When it comes to learning, everyone has both skills and weaknesses. According to Riener & Willingham, 2010) matching instruction to students’ talents and preferences ensures academic achievement.  Differentiated instruction should be tailored to student preferences. Intelligent tutoring systems can assess students’ performance data and identify areas of weakness in order to provide tailored educational support and suggest relevant materials for instruction.

b. Formative Assessment:

The benefits of introduction of artificial intelligence for formative assessment include increased feedback quantity and quality (Gardner et al., 2021). The primary purposes of formative assessment are to provide constructive feedback based on students’ responses, to assist teachers in designing differentiated instructional strategies, and to support students’ self-regulation of learning. Artificial intelligent systems may analyze data to uncover patterns that can be used to produce dynamic, tailored, individualized, and visual feedback automatically (Verma, 2018; Tashu and Horvath, 2019; Lee, 2021, 2023). According to Murphy, R. F. (2019) the use of AI in automated essay scoring frees teachers from the labor-intensive grading process and allows them to assign more extended writing tasks to students. It also automates the grading process, and, more importantly, provide timely formative feedback through the integration of natural language processing-based artificial intelligence.

c. Collaborative Learning:

Collaborative learning (CL) is considered a promising educational approach from the perspectives of both cognitive load and constructivist learning. According to Kirschner, Paas, and Kirschner (2015) learning individually becomes less effective and efficient than learning in a group as task complexity increases. Artificial intelligence provides platforms for learners to connect thus collaborating with one another and enhancing teamwork. Educational collaboration minuses the individual student weakness and ensures maximized group output.

AI’s Role in Student-Centered Learning

Student centered learning approach comes with many advantages unlike the traditional approach were the teacher was the focus and only source of information. A study carried out by Chen and Wang (2023) agreed that artificial intelligent systems can be tailored to suite any education context. Some of the roles of artificial intelligence in relation to student centered learning include:

a. Individualized feedback.

Feedback plays a key role in any educational context. The more accurate feedback, the better the planned action. Timely feedback not only enhances learning but also enables learners to be more focused. Artificial intelligent systems provide individualized feedback pinpointing strengths and weakness which are relevant in determining the next course of action in the instruction process. Unlike the traditional noticeboards that used to display all the results of a class, artificial intelligent systems delivers individual feedback with well-illustrated statistical reports. The feedback is also stored and can be maintained for reference for extended period of time.

b. Adaptive learning pathways.

Artificial intelligent platforms moderates the content delivery and facilitates smooth translation from one competent areas to another. This ensures that learners are constantly challenged and engaged, lowering frustration and discouragement. Thus the learners gradually acquire relevant skills that accommodate varied mastering patterns by means of offering tailored mastering pathways.

c. Personalized educational experiences.

Guided by students achievement databases artificial intelligent systems can proved elaborative feedback. AI can adopt to varied learning methods by using tailored knowledge of pathways, making sure that each student can also get admission to and master the subject at their very own speed. It can recommend areas of improvement and offer congratulatory feedback.

Transforming Teacher Roles

Brown and Martinez (2023) in his study noted that the implementation of artificial intelligence in education requires a major overhaul of the traditional responsibilities of teachers. As mediators of AI-enhanced curricula and data-driven instruction, teachers assume additional responsibilities,

Despite all the advancement of the artificial intelligent platforms the role of a teacher as a coach is irreplaceable by technology. The teacher in any teaching environment acts as a mentor and imparts societal ethics and expectation of the learners. Such mentorship is not only important but helps to nurture the social skills of a learner. Thus the teacher plays the following new roles:

i) From Knowledge Dispenser to Learning Facilitator

AI tools enable tailored learning experiences, allowing teachers to focus on providing extra help and advice to students who require it. It also evaluate student data, detect learning gaps, and provide teachers with real-time feedback on student progress. This enables teachers to tailor their teaching tactics and deliver targeted interventions to meet the requirements of individual students. AI can support collaborative learning by enabling students to share ideas, collaborate on projects, and receive peer feedback. Teachers can serve as facilitators, directing and assisting students in their joint efforts.

ii) Embracing the Role of Mentor:

In mentorship the teacher plays the following roles:

1. Creating Connections: Strong bond among the students can be well ignited by the teacher. The teacher having an understanding of the various temperaments of the students, he is able to cluster them together for the benefit of one another. The teacher can also offer emotional support and keeping a cheerful atmosphere for learning, even with AI’s ability to teach each student individually.
2. Enhancing Advanced Abilities: Repetitive learning and basic skill building can be do by artificial intelligent systems. The teachers thus have time to focus on fostering critical thinking, problem-solving, creativity, and other advanced skills requested by the learners.
3. Understanding Ethics and Society: Teachers unlike the artificial systems interact with the society on daily basis thus understand the societal expectation of a learner. Thus a teacher is well positioned to mentor the morals of the learners.

iii) The AI-Powered Teacher:

Adding AI to education changes what teachers do, making them learn new things and take on different tasks. Patel and Brown (2023) posits that teachers with AI use information to make lessons better for each student, change how they teach, and encourage students to work together. Teachers will have to know how to use AI tools, look at relevant information and plan individualized ways for students to learn. They will also need to help students learn how to think well, be creative, and understand their feelings, which AI can’t do yet.

To make AI work well in education, teachers, people who make AI, and scientists all need to work together. By using AI to make teaching better and help teachers, we can give students a more special, fun, and good learning time.

Case Studies and Empirical Evidence

Since the inception of artificial intelligent, many researchers have researched on its implementation and adaptation in education. Here are some examples and research results that show different ways AI is used in learning environment and how it changes how students learn, what teachers do, and the overall results of education.

Case Study 1: Personalized Learning in Math

Mathematics is one of the subjects that considered challenging. According to Scarpello (2007), 75% of Americans discontinue their studies in mathematics and avoid associated occupations. He cites mathematical fear as a major contributing factor. AI is used to make learning math better for students. Research shows that these AI tools can make students more interested in math and better at it by adjusting the lessons to fit each student’s needs and how they’re doing. Siemens and Baker (2012) found that students who used an AI math tutor understood algebra better than those who learned in the usual way.

Siemens and Baker (2012) looked at how AI tools that change lessons based on how students are doing can help them learn better. They found that these tools, which use real-time information about students to adjust lessons, really helped students stay interested and do better in school. For instance, using AI to change how hard math problems are for each student based on their skills led to better understanding of the subject.

Case Study 2: AI-Assisted Feedback in Writing

Artificial intelligent writing tools have gained popularity in the recent past. This can be attributed to the swift of writing or giving an output. Etaat, F. (2024) observed that writing tools using machine learning are gaining popularity for giving students automatic feedback. These tools examine written papers and pinpoint areas that need improvement after giving a general score. Specific area of improvement are also added to the tailored feedback. Another research by Barrot and Coene (2020) revealed that AI-powered writing tools have arisen to assist users in their English writing processes.

Case Study 3: AI-Driven Assessment in Language Learning

AI is also changing the assessment landscape in language learning. AI-powered language evaluation systems can evaluate students’ speech and writing to provide accurate and objective feedback on their skill levels. AI-driven language evaluations were found to be strongly correlated with traditional human-graded assessments. A study by Tian, Z., Sun, M., Liu, A., Sarkar, S., & Liu, J. (2024), suggested that they have the potential to improve and expedite language learning tests. Artificial intelligence lacks bias thus provides a clear evaluation and assessment of the level of language mastery.

Case Study 4: VR and AR in Learning

Research conducted by Weng, C., Otanga, S., Christianto, S. M., & Chu, R. J. C. (2020) investigated the impact of Virtual Reality (VR) simulations on biology education. The study involved students using VR to explore complex biological processes, such as cellular interactions and genetic mutations, in a three-dimensional virtual environment. Results showed that students who engaged with VR simulations demonstrated deeper conceptual understanding and higher levels of engagement compared to those using traditional methods. It is also cost saving because complex tasks are accomplished within a short time.

Case study 5: Collaborative and Inquiry-Based Learning

Barron and colleagues (2008) investigated how student groupings learnt using digital tools for science projects. They discovered that middle school pupils who collaborated online, sharing data and discussing ideas, improved their thinking and communication abilities and expressed a greater interest in science than those who learnt through traditional lectures. The digital tools not only helped the students to easily comprehend the areas of study but also learning inquiry skills. Further the interest to learn new concepts is greatly enhanced. The more the learners collaborate the more the new ideas or skills acquired.

From the above illustrations it is true that artificial intelligence is positively imparting educational pedagogy and learners’ engagement. However this also comes with a number of challenges as discussed below.

1. One of the most pressing ethical concerns about the use of AI in education is the privacy of students and teachers. Privacy issues occur primarily when people expose an excessive amount of personal information via internet platforms. Accidental or intentional access to the private data may be very destructive.
2. Bias and discrimination are serious considerations in discussions about AI ethics in education. AI systems incorporate existing power hierarchies and biases into machine-learning models. Gender bias is one of the most obvious manifestations of this problem, since it is revealed when students in language learning classes use AI to translate between a gender-specific language and one that is less so.
3. Lack of Interpersonal Interactions: Most students are used with the traditional way of interaction and learning. They prefer physical socialization in the learning process. This cannot be catered for when using artificial intelligence systems.
4. Problems with Technology: Student from poor backgrounds may not manage to buy the relevant tools such as laptops used in modern learning. Some also come from areas with poor network connectivity. Thus the AI systems may not be relevant to them.
5. Job Losses: Individuals without relevant knowledge may not stand out during various appraisals. The labour market including teaching requires person who are technologically enabled and can facilitate learning through various systems.

B. Impact of Emerging Technologies on Learner Engagement

According to Wang, Chen & Anderson, (2014) to ensure and enhance relevant continuous learning, it’s important to diversify our thinking and socializations. Students are not only motivated with the new learning environment but also excited with the positive interactions. New technologies and systems comes in handy to realize this targets.

Traditional classrooms have become more dynamic and engaging by using technologies like augmented reality (AR), virtual reality (VR), gamification, and mobile learning. These technologies and systems not only cater for different learning styles but also individualized preferences. Mo, S. (2011) supported the idea that using technology in teaching and learning greatly boosts students’ interest and focus in class. Thus an enhanced better performance. There are many learning platforms that can facilitate learning while ensuring that the learners collaborate and connect online. One of such is the Edmodo platform. Edmodo is a specific tool that teachers can use to enhance student success and engagement in the classroom (Edmodo, 2008).

Purnawarman et al (2016) define Edmodo as an technology educational platform that allows teachers and students to share content, have online live conversations, take various assessments, and connect with one another. Edmodo enables students to meet their learning objectives even when they are not in school and helps them feel connected to their peers (Yunkul & Cankaya, 2017). According to another research by Al-Said ( 2015), students choose Edmodo because it is easy to use and promotes better communication and learning. Emerging technologies have noticeable impact on learner engagement as discussed below:

i) Enhanced Engagement and Immersion with Virtual Reality (VR)

Virtual reality (VR) and augmented reality (AR) technologies offer students deeply engaging educational experiences by taking them into simulated worlds and adding digital information to their real surroundings. A research conducted by Dalgarno and Lee, (2020) revealed that these technologies allow learners to work with three-dimensional models, visit virtual places, and participate in practical activities that mimic real-life situations. For instance, in medical training, VR allows students to perform simulated surgeries without any risk, improving their abilities and self-assurance (Albion et al., 2020). Likewise, AR adds digital notes and details to real objects, making learning more interactive and interesting (Dalgarno & Lee, 2020).

Virtual reality tools provide students with interactive and hands-on learning experiences that involve them in practical exploration and real-life simulations. VR allows students to engage with realistic settings and carry out virtual experiments. (Albion et al. , 2020).

ii) Motivation and Achievement through Gamification

Gamification involves adding game-like features to educational settings to make learning more fun and exciting for students. It uses things like points, badges, and leaderboards to turn learning into a game. This approach helps students feel a sense of accomplishment and keeps them interested in their studies (Deterding et al., 2011). For example, apps that teach languages use gamification by letting learners level up and earn rewards, which encourages them to keep practicing words and grammar.

iii) Flexibility and Accessibility with Mobile Learning

Mobile applications take advantage of the portable handheld devices such as smart phones and tablets to support learning any time, any place. Portability in mobile devices provides an extension of learning to the students in that they are able to access learning materials from any location irrespective of the classroom environment (Kukulska-Hulme & Traxler, 2013). Whether in the car on the way to school, waiting for an appointment, or riding a bus or a train, the student can use his or her mobile device to carry out learning activities thus making good use of time and opportunities.

Furthermore, Mobile learning platforms foster differentiation of learning mechanisms and approaches since the learners can choose their preferred courses according to their needs, interests, as well as learning abilities. As in the case of Web 2.0 applications, mobile applications and platforms are able to furnish learners with interactive and multimedia content that is even tailored to the learner’s pace and knowledge advancement (Papastergiou, 2009). The students can get acquainted with topics of their concern, review the concepts that initially created difficulties for them, and carry out the individual learning activities which follow the principles of independence and free to learn.

Furthermore, Mobile learning takes into account such features of learning as learning mode and style, learning style and ability. Mobile devices can handle text, audio, video, and interactive simulation, which catering for students’ learning style as preferred by the students (Kukulska-Hulme & Traxler, 2013). For instance, children who learn differently when their ears are used would enjoy educational podcast, while those who find their eyes useful in learning would enjoy the interactive and perhaps appealing, diagrams and other additions.

Moreover, Mobile learning is context aware and thus it is meshed into students’ everyday lives behavior, eliminating a clear line between learning and non-learning. Largely, mobile devices have become a necessity in today’s society and can be described as essential communication, work, and entertainment aids (Papastergiou, 2009). Thus, considering students’ use of mobile technology, educators can create connections between what is taught in class and their everyday experiences, thereby enhancing the applicability of knowledge.

Last, Mobile learning supports collaboration; this means students find opportunities to communicate with other students and instructors in virtual environments. Other and social networks enable real-time and non-real time communication including text, forums and video and enable peer to peer and group communication (Kukulska-Hulme & Traxler, 2013). Introducing mLearning to collaborative activities enhance sharing of knowledge, problem solving and socializing which in-a-way enhances the learning process of students.

iv) Better Academic Outcomes

Conducting a meta-synthesis on new technologies’ implementation in learning context revealed that new technologies have a positive impact on classroom achievements of learners across all the learning domains. According to previous meta-synthesis research works, instructional interventions facilitated through technology have shown positive outcomes on students’ performances and knowledge and skills mastery (Hattie, 2012; Tamim et al. , 2011). Formative assessments like online quizzes and simulations facilitate timely evaluation on the progress of the students and the provision of timely feedback to enhance improvement (Wiggins, 2012). Learning analytics and data analytics supplement the knowledge about the students’ learning behaviors and performance so that instructors can adapt their teaching strategies according to the learners’ characteristics. Offering differentiated instruction and supporting the continuous evaluation of the learning, emerging technologies in learning improve academic performance and learners’ success in learning contexts.

New technologies like artificial intelligence (AI), virtual reality (VR), gamification, and mobile learning have redefined the conventional patterns of learning and teaching practices transformed the instructional techniques that can improve the students’ outcomes.

v) Personalised Learning with Artificial Intelligence

Machine learning makes it possible for students to receive differentiated learning as a result of identification of each student’s preferred mode of learning as well as his/her needs. An Intelligent Tutoring Systems is defined as a system that employs artificial intelligence techniques in order to offer a learner with personalised instructional content, feedback and learning procedures based on his/her learning experience and learning model (Anderson, 2019). Smart technology, since it delivers formative feedback and remediation to individual learners, enhances every individual student’s achievement and mastery of academic content (Tamim et al. , 2011).

The future of Technologies on Teaching and Learning

a) Growth of microlearning and adaptive learning platforms: Therefore, microlearning – a large branch of short, structured learning activities – and adaptive learning platforms, which alter material based on a learner’s performance rate, could rise.

b) Block chain for academic achievement verification and credentialing: They may correlate its application with the safe accomplishment verification and the academic credentialing uses of block chain technologies. This possibly could improve security of academic documents and at the same time increase proficiency in activities like degree attestation.

c) Improved data analytics for individualized insights: As techniques in data analytics continue to spread so will most likely the ability of an instructor to learn about the students from the accumulated data regarding their performance and learning behavior. It can be useful in developing staking out interventional approaches, making decision on instructional approaches among other areas.

d) Greater usage of game-based learning and gamification: To raise the level of students’ interest and motivation such elements and features as use of game-based learning techniques or features of the gamification may be integrated to educational platforms on a larger scale.

e) Creation of instruments for social and emotional learning (SEL): Therefore, it can be stated that educational technology could mean building of social and emotional skills to a certain degree. Students might benefit from the utilization of the SEL resources and applications in improving their interpersonal and students’ self-awareness and occurrence of resilience.

f) Enhanced cybersecurity in edtech: Because safety of educational information and the students’ data is of significance, cyber security measures are further expected to receive attention. .

However, the concept of expounding and integrating emerging technologies in education has the following challenges and issues. Issues like digital divide, privacy, and technology support are some of the concepts that hinder students’ equal opportunities and access or their active involvement in learning technologies environment as described by Warschauer & Matuchniak (2010).

Also, because of the high rate of advancement in technology it becomes mandatory for teachers to update on how to use the technologies for teaching (Ertmer, Ottenbreit-Leftwich, & Tondeur, 2015). However, analysis and criticism in terms of technology dependency, and decrease in face-to-face interaction and thinking skills remain persistent (Turkle, 2015).

C. Technical and accessibility Issues relating to artificial intelligence in pedagogy and learner engagement

i) Infrastructure Requirements:

Common to many AI applications in education, there is usually a need for strong IT support, fast internet connection, and quality equipment. This means that there can be disparities between schools and areas hence restricting some pupils. Thompson and Williams (2023) stated that solid technology support is required to enhance the initiation of AI-based educational applications. This ranges from efficient information processing machinery, reliable internet connection as well as secure storage facilities for the information. Indeed the above researchers argued that the ability for growth and extensiveness of the AI applications in educational settings can be limited by the absence of proper support facilities. As a result, it can affect the functionality of the apps to deliver feedback to instructors and students and offer personal learning paths.

ii) Data Confidentiality and Protection

Training and enhancement in AI often take place with the help of vast data sets. Data privacy and credentials of students coupled with rule and regulation like General Data Protection Regulation (GDPR) must not be compromised. This may leave essential information within artificial intelligence systems in wrong hands. The employment and collection of student information by artificial intelligence learning systems are troublesome ethically and legally according to Davis and Brown (2023). Davis and Brown put emphasis on the necessity to have written documentations of policies and procedures which protect the privacy of students, prevent unauthorized access to their information, and ensure compliance with the law.

iii) Digital Divide

In particular, if technology is not designed fairly then the use of AI in education seems to exacerbate existing injustice. Disappointingly, the students who have never had a stable internet connection or proper equipment will be affected. Johnson and Martinez (2022) asserted that score cards’ evidences reveal that students of different populations are not equally equipped with technological gadgets and many of them are lacking reliable internet connection especially in disadvantaged areas. Hence, this digital divide hinders the usage of products necessitating a strong technological platform hence aggravating the gap, especially in terms of education.

iv) Difficulty in Understanding AI

Generally, some of the internationally acclaimed AI-based educational programs can be somewhat complicated to use or to comprehend which is a downside to teachers and students who may not be so tech-savvy. In their study, Lee and Garcia (2023) reveal that many teachers are overwhelmed by the confusion of how AI algorithms work and how to properly assess the outcomes. Thus, mainly due to the fact that AI is based on fragments of such disciplines as computer science, statistics, cognitive psychology and other it is rather problematic to comprehend. From the argument by Brown and Davis (2022), it is evident that the complexity of the AI algorithms and how their inputs influence the outputs regarding education significantly hinders the comprehension of how AI operates and how its outputs impact learners’ learning gains. Due to the nature of AI being complex, the educators must be well trained if not professionally developed in the use of AI intelligently in class.

v) Language and Cultural difference

AI that has been created for education within a specific language or culture will not function effectively in a multilingual/multi-cultural environment. Thus, the enlargement of the tools, to meet the language and culture that it may adopt, forms a core component of outreaching it. According to Li and Patel (2023), the current AI systems meant for the learning environment can prove to lack adequate sensitivity to language differences as well as cultural idiosyncrasies within a culturally diverse learners’ population. This oversight can cause AI bio educational technologies in reducing diversity in students and cultural diversity through raising barriers to communication and understanding.

vi) Special Educational Needs (SEN)

 AI programs should have provisions for students with SEN which covers the physical disable and learning disable students. AI tools should be easily available as well as expansible enough to fit different learning needs. In this case, Martinez and Chen (2023) point that there is a need to discuss how to make AI-based educational tools available to learners with impairments. They emphasize the importance of recursive and integrated user interfaces and aspects such as assistive technology and the adherence of accessibility standards for learning.

vii) Ethical Considerations

Speaking of the application of AI in learning, there are some key questions that can be considered as ethical dilemmas. Some of the questions concern methodologies and practices with regards to student data, the level of openness of AI decision-making, and how AI can support and enhance students’ communication and learning, instead of merely substituting people and processes. Jones and Smith (2023) observed that the use of AI technologies present complex ethical dilemmas with most of them falling under the themes of responsibility, transparency and fairness. Jones and Smith have stated that to ensure that the technologies in education powered by Artificial Intelligence uphold moral standards, and offer equal educational opportunities to all students, educators and legislators need to overcome these three challenges.

viii) Tool Familiarization

Training courses should provide students with practical experience in the use of AI-based instructional materials that are relevant to the topics and the learners’ grade levels. In our work, Patel and Johnson (2023) have pointed that using AI-assisted educational technologies by teachers is highly dependent on the improved educational training programs or courses, and the continuous professional development schemes. Following this notion, Nguyen and Smith (2023) claimed that for enhancing instructors’ competency with AI-based learning tools there is a need for training interventions that can effectively address the focus on implementation and applicative experience. According to Nguyen and Smith (2023) the current offers on scale-up solutions should be accompanied by continuing professional development that targets the variability in technological literacy and instructional requirements among teachers.

Strategies for Effective Teacher Preparedness

1. Professional Development Programs:

The instructor should organize timely continuing professional activities to ensure instructors familiarize themselves with new developments in AI and enhanced practices. Support the formation of collaborative learning environments through which the educators can share encounters and strategies of AI adoption.

2. Hands-on Workshops and Simulations:

Most of the instructors are unfamiliar with the use of AI and how it when applied can enhance teaching and learning this makes it essential to organize sessions which can act as a sandbox in which the instructors can play around with the AI tools and simulations with an aim of realizing their strengths and suitability in teaching.

3. Supportive Resources and Communities:

The Resource Libraries contain a number of the resources, examples of cases and lesson plans as to how some of the uses of AI may be illustrated across a range of fields and situations.

Develop communities that enable educators not only to get instructions on where to look for the necessary information, but also to discuss significant ideas and collaborate on new approaches to AI technologies in education.

4.  Feedback Mechanisms:

Identify the procedures of collecting information regarding the impact of AI tools from educators. This means practice the difficulties and thus enhance the training programs in order to address the treatment discrepancies.

5. Policy and Leadership Support:

Properly equip the educational policies with funds, capacity, and templates for the most suitable use of AI.

The results also suggest urging school leaders to include AI training as one of the components of continuing professional development and provide relevant resources.

Being able to shift and shape teacher training to encompass the concept of Artificial Intelligence, in-depth is an indication that many educational organizations are in a position to be ready to assume the mantle of preparing students for an AI society even as it answers the numerous questions that could come its way.

CONCLUSION

There is a strong possibility that the teaching and learning processes into which emerging technologies are introduced could be revolutionized by the innovation to deliver personalized learning, promote participation and boost the academic results. Nevertheless, the implementation of these advancements depends on comprehension of the principles of pedagogy, the learner’s requirements, and the ethical issues concerning their application. It might be committed to the exploration of existing problems and the implementation of new technologies to fruitful up-to-date educational transformations.

It is interesting to understand that the combination of artificial intelligence (AI) with teaching approaches is promising to revolutionalize the teaching and learning processes as well as the outcomes of the education systems. This article has attempted to examine the various interventions of AI to teaching and learning in relation to how AI technology can support conventional teaching practices, promote learning that is student-centred, as well transformation of the teacher’s role.

This means that the incorporation of AI has the potential to greatly enhance the processes of learning as well as the outcomes in that learning process in terms of efficacy and efficiency since personalization, flexibility and ease in tasks such as grading and feedback can be achieved. This frees up teachers to concentrate on their main duties: enhancing students’ participation; promoting the formation of argumentation concepts; and enhancing creativity.

However, at a same note it is important to accept the fact that the incorporation of artificial intelligence in education comes with its own unique problems. Examples of these are data privacy, which is a critical ethical aspect that has to be addressed in AI and other aspects of bias in the algorithms that are used in such programs. Further, one must ensure proper use of AI solutions as for solutions for an essential role of human communication in education.

In the future, the AI in education is in establishing proper and moral structures of AI in order to protect children’s interest and provide positive learning environment to each child. Teachers, technology producers, and governmental providers need to work with each other and participate in research activities to discover the AI’s potential for education and use it as a tool for enhancing the education processes.

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