1 Introduction

Smart technology, a type of technology which interacts with other technologies and operates remotely, use the Internet and cloud connectivity for simplified tasks (Sovacool & Furszyfer Del Rio, 2020). In education many educators make use of Artificial Intelligence (AI), a technology which uses smart technologies to support pedagogical decisions, deliver meaningful and successful instruction, and manage assessments (Aazam et al., 2018; George & Wooden, 2023; Zawacki-Richter et al., 2019). The value of AI and its use has increased across a wide range of application sectors, from engineering, business, security, aviation, and including education (Escotet, 2023; George & Wooden, 2023; Zhang & Lu, 2021). According to Waghid and Waghid (2018), in contemporary times, educational technology is not merely a facilitator of conventional learning outcomes but is also critical in developing both educators’ and students’ flexibility of mind and creativity, together with a network of connections, all of which are important for in a collaborative educational environment. Thus, in the education context, newly developed smart technologies enable educators to use AI for grading, personalised learning, and other cutting-edge ideas to help students optimise their learning These technologies include interactive whiteboards, smartphones, Chat Generative Pre-trained Transformer (ChatGPT), Virtual Reality, Massive Open Online Courses (MOOC), such as those offered by Coursera, Udemy, Edx, Third Space Learning, and Learning Management Systems (LMS), as well as technologies for developing and implementing instruction, like Quizizz, Kahoot, and Google Classroom. These have been found to enable educators to use AI for grading, personalised learning, and other cutting-edge ideas to help students optimise their learning (Alam, 2022; Adiguzel et al., 2023; Lee & Lee, 2021; Tapalova & Zhiyenbayeva, 2022).

The rapid developments of new tools, such as Internet-enabled technologies, have transformed, and continue to change modes of instruction in higher education (Okunlaya et al., 2022). These tools afford educators new ways to engage with students, to explore and use innovative teaching strategies, and to foster a learning environment where students take ownership of their learning (Grassini, 2023; Marshall & Kostka, 2020). Currently higher education institutions in the global context rely extensively on some or all of the aforementioned technologies to accomplish educational goals and professional development with the aim of successfully meeting teaching objectives (Chan, 2023; Tondeur et al., 2017; Yuan & Powell, 2013). AI continues to gain popularity in higher education, offering solutions to teaching challenges, addressing knowledge gaps, preparing students for lifelong learning, and ensuring successful retraining efforts for educators and students (Abiodun, 2023). Examples of the various ways in which AI enhances learning include the provision of tutoring, personalised instruction, accessibility, and the comprehension of course materials (Bucea-Manea-Țoniş et al., 2022; Al Husseiny, 2023). It further cuts down on time wastage by automating administrative tasks and streamlining administrative procedures. It also improves assessment methods, and promotes remote learning (Bucea-Manea-Țoniş et al., 2022; Al Husseiny, 2023).

Waghid et al. (2019) underscore the value of technology in promoting a worldwide educational perspective, one that equips students with the technological skills, critical thinking, and ethical reasoning needed in a rapidly technologizing environment. Integrating AI in education has led to the adoption of Smart Technologies Supporting AI in Education (STAI), in the process promoting both learning-by-doing and independent discovery amongst students. However, several developing nations in Africa face challenges in the form of limited awareness, and cultural, economic, and infrastructural obstacles, all of which tend to hinder the successful implementation of STAI in higher education. This is particularly the case in countries like Lesotho, Nigeria, and Rwanda (Abiodun, 2023; Ochieng et al., 2019; Olatunde-Aiyedun & Hamma, 2023; Sibal & Neupane, 2021). Resistance to the use of STAI in educational instructional practices on the part of educators in higher education is a topic of discussion on the use of AI in education in these three countries (Abiodun, 2023; Ochieng et al., 2019; Sibal & Neupane, 2021). Some of the opposition is stoked by the lack of knowledge of the educators themselves, together with their lack of expertise in creating and delivering instruction using smart technologies that align with AI in education (Barakabitze et al., 2019; Owan et al., 2023; Olatunde-Aiyedun & Hamma, 2023).

Additionally, some educators in these three countries have been found to be unaware of the technical aspects of using AI in the classroom, despite the fact that several studies have recommended that educators need to be familiar with cutting-edge technology to enable effective user satisfaction and ease of understanding of subject matter (Olatunde-Aiyedun & Hamma, 2023). Other studies have found that most ‘smart’ classrooms in higher education in these three nations are in need of upgrading to support STAI effectively in higher education (Essel et al., 2018; Olugbade et al., 2023). However, the three nations have distinct socioeconomic, technical and infrastructure issues that exacerbate deficiencies in the timing and manner of integrating contemporary technologies into teaching methods in the classroom.

Empirical evidence shows that Lesotho, Rwanda, and Nigeria each have unique difficulties when utilising technology in their higher education sectors (Essel et al., 2018; Nsabayezu et al., 2022; Olugbade et al., 2023; Turugare et al., 2020). For instance, issues such as the unpopular and novel use of AI in education, a lack of technological know-how in the use of contemporary technologies for instructional purposes (development, teaching, and lesson assessment), ethical issues, user attitudes, governmental policies, poor funding, inadequate infrastructure and network connectivity have all been reported by Gumbo and Nkala (2023), Kolog et al. (2022), Nja et al. (2023) and Olugbade et al. (2023) as major concerns mitigating against the use of AI or STAI in higher education in these three countries.

Although AI has been found to be assisting in the improvement of the standard of living and the economy in Rwanda, Lesotho, and Nigeria, challenges peculiar to each of these countries remain to be overcome before their governments are able to offer up-to-date education, and in particular tech savvy educators, together with the opportunity to be aware of and maximise the practical uses of STAI in higher education. The potential for STAI to creatively support learning and knowledge development in higher education in the three countries needs to become a desirable reality for all stakeholders in education, even though we noted earlier that the use of AI in education remains in its early stages.

Furthermore, the lack of awareness and technical knowledge to support educational practices with contemporary technology of educators is a barrier to AI in education in all three countries (Kanbul et al., 2022). In addition, inadequate digital literacy among these educators serves to increases the barrier to their use of AI in higher education (Gumbo & Nkala, 2023; Kolog et al., 2022; Nja et al., 2023; Olugbade et al., 2023). As a result of these barriers, we submit that some educators have come to resist technology and its use in their pedagogical practices, because they see it as a burden that they do not need in addition to their academic and professional responsibilities. Although we posit that incorporating STAI into instructional practices has a strong potential to reduce teachers’ workloads, to increase learning opportunities, and to provide a means of implementing effective teaching strategies in the classroom, we envisage that the over-reliance on such smart technologies could place at risk traditional educator roles such as those linked to assessment, curriculum design and planning responsibilities, student support, and guidance of educators. We argue that a balanced approach that harnesses the benefits of AI to support, rather than replace, traditional roles is needed to both support and enrich a range of teaching and learning modes.

We anticipate that educators’ negative perspectives of the use of AI in teaching tasks are likely to shift through their own exploration of the use of STAI in education, particularly in higher education, where all specialisations in the countries’ economies are developed. We also anticipate that, by learning about the empowering ways in which smart technologies can support AI, educators may advance their technological pedagogical knowledge to help develop or advance their students’ cognitive skills and content knowledge. It is hoped that, through an understanding of the participating educators’ viewpoints on the advantages and disadvantages of incorporating cutting-edge technology, this study’s outcomes will assist in closing the AI education gap, particularly in the usage of STAI in higher education. However, we postulate that some issues to do with integrating STAI came to light as this present study’s findings emerged and these have the potential to either hinder educators from, or encourage them towards keeping up with the current global shift in educational practices.

In light of the aforementioned literature on this topic, it is clear that further research is necessary in order to investigate the ways in which educators in the aforementioned countries perceive and view the use of STAI in their classrooms. We note that even less research has been done to explore cross-country perspectives of AI in education, perspectives that may be applied to represent the phenomenon under study in higher education across Africa. The practical application of AI, specifically in developing countries such as Lesotho, Rwanda, and Nigeria, has sparked discussions about its potential for use in higher education in those countries. Moreover, there exists a paucity of literature that integrates the most recent knowledge of smart technologies that support AI in higher education within the contexts of higher education in Rwanda, Nigeria, and Lesotho. To understand educators’ perspectives in those countries on the use of STAI in teaching and learning, as well as to identify the challenges associated with integrating STAI into higher education instructional practices, a study that explores in depth the specific higher education contexts of the three countries is imperative. To address this issue, the current study’s objective was to ascertain what sampled groups of educators from Lesotho, Rwanda, and Nigeria, at the time of the study, were thinking about the application of smart technologies which support AI in higher education. This study aimed to answer the following research question:

  • What are the viewpoints of sampled groups of Lesotho, Rwandan and Nigerian educators on Smart Technologies Supporting AI in higher education?

2 Theoretical perspectives: Vygotsky’s social-constructivist learning theory on the use of tools in teaching

Creaghe (2019, p.17) argues that “one shortcoming of the Piagetian approach is that, in contrast to the Vygotskyan sociocultural approach, it downplays the importance of tool and interaction in knowledge development”. To critique Piaget’s theory of knowledge development, in 1934 Vygotsky developed social and cultural theories from his research on teaching and learning, including his social constructivist theory (Vygotsky, 1978). The social constructivist theory includes different components such as social interaction, scaffolding, community of practice, mediation, tools, language, culture, and the zone of proximal development (ZPD).

These components of the socio-constructivist theory, including scaffolding (knowledge gained through the support or demonstration offered by the educator), zone of proximal development (reusing and developing knowledge gained through the help of a more knowledgeable person), mediation (social interaction between educators and students aimed at enriching students’ learning experiences), cultural tools. In the case of the current study, we reason that the use of a cultural tool such as technology could aid in the development of cognition that help mediate higher-order mental processes such as reasoning and problem solving. In the book “Handbook of Research on Social Software and Developing Community Ontologies,” Code and Zaparyniuk (2011) provide a thorough explanation of how cognition develops through the application of cultural tools in knowledge development process. Among the “tools” that Code and Zaparyniuk (2011), Hsu (2005), and Pea (2018) identified as cultural tools that can facilitate cognition development in socially driven learning environments are computers and software applications. However, Pea (2018) and Mohamad Nasri et al. (2023) contend that, while the social constructivist theory holds that knowledge can be co-developed through social interaction and reinforced by ‘tools’, for purposes, in the specific context of this current study, we reason that it falls short of explaining how and what educators think about the ways in which institutional, environmental, instructional, technical, and infrastructural factors could enhance or constrain the use of technology in higher education’s instructional processes.

In order to bridge this theoretical gap, the components of social interaction and technology (as a tool for the twenty-first century: beneficial for improving critical thinking, problem-solving, cooperation, and digital literacy) were used in this study to explore and draw conclusions from the viewpoints and experiences of a sampled group of educators on STAI and its use in teaching. This includes the use of STAI in the design, implementation, and evaluation of higher education instruction. In addition, the study adopted the two components of Vygotskyan social-constructivist theory to understand how the participants were using technology to optimise the effectiveness of their teaching, thereby improving their students’ academic performance. It is essential to explain that the components of socio-constructivism theory were adopted in this study as a lens through which to explore participants’ teaching and learning experiences when using technology, and the roles technology plays in facilitating the kind of interactive learning environment that leads to measurable cognitive development. Moreover, from the perspective of sampled groups of educators in higher education in Rwanda, Lesotho and Nigeria, the theory’s components helped us to understand how interaction with technology in instructional practices was helping educators and students generate new knowledge and make learning an engaging and cooperative process, one in which both educators and students were able to identify problems, learn from one another’s viewpoints, and work together to propose solutions.

Additionally, the use of Vygotsky’s social-constructivist theory of knowledge development is consistent with recent studies conducted on integrating technology and knowledge development vis-à-vis social interaction in teaching dimensions (Souza & Amaral, 2014; Freire, 2020; Theodorio, 2023). Gutierrez-Bucheli et al. (2024), for instance, made use of social constructivist principles in their research and argued that the teaching dimension, including the use of technology, aids the designing of different pedagogical decisions aimed at accomplishing curriculum objectives. The ideologies of Lasaiba (2024) and Haleem et al. (2021) align with the ideas presented by Gutierrez-Bucheli et al. (2024) regarding the incorporation of technology and social integration in any educational process that supports pedagogical strategies meant to transform ways in which knowledge is created, disseminated, and repurposed.

In addition, Hosen et al. (2021) linked the advantages of technology and social interaction in the knowledge-building process to improve student commitment, engagement, and visual comprehension; the most important link has been made with the development of knowledge from information exchange. The theoretical framework of the study done by Hosen et al. (2021) echoes Vygotsky’s (1978) argument that teachers can stimulate students’ cognitive development by using an appropriate cultural tool (such as technology in the 21st century) in lesson planning. In other words, learners’ cognitive abilities can be developed throughout the educational process to support self-development and their understanding of their own learning abilities and limitations.

Furthermore, in their responses to the semi-structured questionnaire, adopting tools such as STAI, aided a shared understanding of the subject matter, together with the challenges, and opportunities for using technology in the classroom. Not only that, but participants’ responses provided insights into how sampled groups of educators made use of STAI to support teaching processes aimed at acquiring or supporting teaching/pedagogical knowledge (Wang et al., 2011).

We argue that, when STAI are integrated into educators’ teaching methods, a teaching and learning environment that focuses on implementing the twenty-first century’s standards of teaching is created. We refer to twenty-first century’s standards of teaching as the capacity to integrate critical thinking, creativity, digital literacy, and practical experience within the context of basic subjects (Ata & Alpaslan, 2024; Van de Oudeweetering & Voogt, 2018). We also contend that using the tool technology in the Vygotsky (1978) sense, of which STAI is a part, can assist in the designing and implementing of student-centred instruction and active participation. We equally reason that using STAI may open up opportunities for educators to advance their technological skills, and become knowledgeable about making different technological pedagogical decisions that enable students to independently and collaboratively acquire and personalise subject matter knowledge. It also transpired that the use of the Vygotskyan theoretical lens in this current study assisted us in identifying the challenges involved in understanding the uses of STAI in the higher education institutions of Lesotho, Nigeria, and Rwanda.

We also draw knowledge from the literature which supports social constructivist theory, and from the perspectives of scholars arguing for and recommending the use of AI in Lesotho, Rwandan and Nigerian higher education contexts. For instance, Ayanwale (2023) and Khomokhoana and Kogeda (2021) argue that little research has been done on the desire to learn AI in Lesotho, despite the fact that smart education is becoming increasingly important globally in enhancing learning opportunities and affording students the tools they need to deal with societal, technological, and environmental challenges. Similarly, we applied the theory to help us understand the claims made by Masabo et al. (2023) that the Rwandan higher education system could use AI-enabled technologies to identify underperforming students and support them in developing technological skills to necessary to attain academic excellence.

Furthermore, we note from Thomas and Gambari’s (2021) arguments that educators in Nigeria need to imminently embrace technology for the purposes of advancing teaching, research, and lesson assessment in the Nigerian higher education system. Thomas and Gambari’s ideas helped us understand that using smart technologies supporting AI in higher education assists in designing and implementing technology-enhanced assessments as well as lessening the burden of grading assignments. These thoughts resonate with Waghid and Waghid (2016), who avers that the digital technology integration in higher education, grounded in critical theory, underscores a paradigm shift from traditional outcomes-based assessment practices to assessment practices aimed at ascertaining critical insight and communicative meaning-making. Pulling back the curtain on this conversation phase, the study’s application of theory helped us comprehend the technical, cultural, and financial prerequisites for integrating STAI in higher education. Above all, the theory provided useful data from which we could infer how STAI can influence effective teaching strategies in higher education.

3 Methodology

This qualitative study was underpinned by an interpretive paradigm (Bertram & Christiansen, 2020). Interpretive research provides a process for researchers acquire the kind of data that helps them to make sense of their research participants’ meanings, understanding, thoughts, and experiences when the researchers use a specific research plan and examine the findings from their research (Bertram & Christiansen, 2020). In other words, using the qualitative and interpretive approach enables an action or event to be analysed based on the participants’ understanding, perceptions, convictions, and values (Theodorio, 2023). Drawing from these arguments, we argue that the worldviews of interpretivist researchers are predicated on the participants’ subjective opinions, obtained as insights into the phenomenon under study. Based on these claims, this current study employed an interpretive research methodology to gather and disseminate information on educators’ experiences and views of Smart Technologies and their role in supporting STAI in higher education. The data presented in the study is drawn from the experiences and views of sampled groups of educators in Rwanda, Lesotho, and Nigeria respectively.

4 Sampling

Purposive sampling was employed in the study to select the participants who were to provide the qualitative and interpretative data required to address the research question. Most importantly, this study used a purposive sampling technique to select participants and gather a plethora of information on the subject matter specifically from educators teaching in a higher education setting. To be selected as study participants, participants needed to meet the following criteria: they needed to be educators in higher education in Rwanda, Lesotho and Nigeria respectively; participants needed, at the time of the study, to be utilising, or attempting to utilise, smart technology in their lesson planning and classroom implementation, and participants should be willing to answer the online semi-structured questionnaire. Despite our initial target of having 350 or more participants, 115 individuals participated in the study.

5 Research site

The use of smart technologies which support AI in higher education was the subject of discussions in meetings held online between the researchers from Nigeria, Rwanda and Lesotho, all of whom are members of a community of practice. It was unanimously agreed to have meetings online to overcome the geographical distance barrier. Attending meetings online also offered flexibility and convenience with regards to location. Furthermore, holding meetings online promoted inclusivity and helped lower the number of obstacles to physical accessibility. The meetings were held using WhatsApp voice calls. The outcomes from the meetings include agreements to consider and share suggestions for STAI in higher education, the designing and methods of disseminating the data collection instrument (online semi-structured questionnaire) to members of the research team from across the three countries in ways that could help with the process of completing and spreading the research among all educators in Rwanda, Lesotho and Nigeria.

6 Research instrument

Data was collected using an online semi-structured questionnaire on Google Forms. Since English is one of the official languages of the three countries (Nigeria, Lesotho, and Rwanda), the questionnaire was developed in English. The questionnaire had twelve questions, four of which were closed-ended and eight of which were open-ended. The closed-ended questions cover information topics such as the name of the country in which the educational institution of the participating educator is located, the kind of AI-supporting smart technology being utilised in the education institution of the participant to plan or provide instruction, and whether or not the respondent considered that these technologies were facilitating the efficient and speedy completion of education-related duties. The open-ended questions also cover topics, such as how STAI is used to manage course loads and assist in the making of various pedagogical decisions, how STAI is used to promote concept learning both inside and outside of the classroom, the potential applications of STAI in education, challenges that stem from STAI that could potentially undermine subject matter instruction, and how participants perceive these challenges being overcome.

The participants (educators) from Nigeria were the first to complete the semi-structured questionnaire, followed by Lesotho and Rwanda respectively. This sequence assisted the researchers in understanding how the participants sequentially provided their responses. However, we intentionally omitted identifiers of individuals, such as their names, gender, institutions and electronic message addresses in the semi-structured questionnaire in order to respect rules of anonymity.

The level of students being taught, types of smart technologies being incorporated into teaching, utilisation of smart technologies in teaching, and participants’ views on potential strengths and challenges linked to the use of STAI were questions which made up the online semi-structured questionnaire. Before the participants filled out the semi-structured questionnaire online, the researchers ensured that it included a section which requested a participant’s consent. Then, we assured the participants that the outcome of the research would only be used for research purposes and might be shared without t financial gain.

Figure 1 shows a total of 115 respondents from the three countries completed the online semi-structured questionnaire. The results confirm that 46.6% of the participants were from Nigeria, while 26.7% of the participants were from Rwanda and Lesotho respectively.

Fig. 1
figure 1

Participants’ distribution over the three countries

Full size image

7 Data analysis

All the data was collectively collected at the same time, and then thematically and inductively analysed (Creswell, 2013). ‘Inductively analysed refers to the process we followed to collect and analyse the data. That is, we utilised an online semi-structured questionnaire to gather data which was then analysed to identify themes that would, when coded, provide answers to the research question (Karahan et al., 2015; Wang, 2024). The word collectively and the phrase ‘at the same time’, refer to the eliciting of all participant responses simultaneously, following the expiration of the allotted response time. The analysis process started with the conversion of the excel worksheet containing the participants’ responses into a Microsoft document using https://online2pdf.com/convert-excel-to-word#. Subsequently, the file was formatted and checked against the original data source to identify errors and track responses to ascertain who had said what and why.

The file was later uploaded onto the Computer Assisted Qualitative Data Analysis Software, NVivo for thematic analysis. Using NVivo software, participant responses which referred to tablets, cloud computing, learning management systems, video conferencing and Turnitin were coded as ‘smart technologies used in instructional practices’. The researchers made a note of these technologies to indicate the smart technologies supporting AI, and three themes were consequently coded as (1) STAI in Higher Education, (2) Using STAI in Higher Education, and (3) STAI Challenges in Higher Education.

In addition, we coded participants’ identities as E1N, E2N, E3N, E4N, E5N, E6N, E7N…….E55N to represent participants from Nigeria, E1L, E2L, E3L, E4L….E30L for educators from Lesotho, while participants from Rwanda were coded E1R, E2R, E3R, E4R…E30R. All of the participants were encouraged to respond to the questions without fear and were not at any time coerced when giving their responses. The researchers also ensured that the data gathering and analysis processes were consistent with the Vygotskyan social constructivist theoretical framework informing the research.

The precision, consistency, authenticity, richness, reliability, and legitimacy of the data generated and gathered during an investigation are all considered to be validity criteria in any research, including educational research (Cohen & Morrison, 2018). Flick (2018) advocates the triangulation of various instruments used in data collection to assure the accuracy, reliability, and authenticity of the data generated and collected in research studies. In following these methodological guidelines, we the used an online semi-structured questionnaire and the authors’ notes as data collection instruments. Additionally, we considered that the respective strengths of the two instruments offset each other’s weaknesses. The questions were peer-reviewed by the researchers prior to instrumentation. The data gathered was coded by the research assistants who submitted the coded data to the researchers for discussion and member verification. However, during the data presentation, as identified in the previous section, participants’ identities remained confidential.

8 Findings

As mentioned earlier, ‘Using STAI in Higher Education’, ‘Challenges of STAI in Higher Education’, and ‘STAI in Higher Education’ are the themes that emerged during the coding of the data collected using a semi-structured questionnaire. The study’s findings, and the discussion thereof, are based on these three themes. The following headings encompass the discussions on the themes:

9 STAI in higher education

In our study we examined the participating educators’ perspectives of the use of STAI in certain higher institutions of learning situated in Nigeria, Lesotho and Rwanda. The findings show that the participants identified a range of different STAI that they considered could be advantageously incorporated into instructional practices. According to our reasoning, based on the data which emerged, incorporating STAI was subject to the participants’ preferences, technological skills, teaching objectives and affordability.

For instance, E21R wrote:

I know of Internet-Enabled Tablets, Chatbox, smartphones, smart boards, Interactive Whiteboards and learning management systems as technologies I can use to learn content of subject matter, upload, interact, support and encourage the students to interact and learn concepts in and out of classroom instructional practices.

Affirming E21R’s response, E2L indicated that:

Internet-enabled Laptop, smart cell phones, wireless data projectors and learning management systems are technologies that are now used to support teaching and learning purposes in the institution.

E38N identified other types of smart technologies which s/he perceived to be supporting artificial intelligence in education:

Kahoot, Google Classroom and Quizziz are software I use to simulate concepts, teach, assess and provide instant feedback on instruction.

In similar vein, E4R identified “smart TV and projector, Microsoft teams” as technologies that s/he considered could be incorporated into online and classroom instructional practices. Although E55N indicated that s/he was not using smart technologies to organise and present instruction, it transpired that the responses of E29N, E13L and E19R further indicated that, in their view, cloud computing and mobile technologies could be used to support teaching and learning in the various ways/styles that students learned, retrieved or saved content online, processes which, in many ways, these participants considered would assist in learning from anywhere, and anytime. The participants were of the view that, by using cloud computing, for instance, a student could learn the content of subject matter in the same way, and at the same level it is being learned in developed countries.

Although the data gathered offered a strong indication that the use of STAI is a positive development in higher education instructional practices, the responses given by E1N, E4N, E5N, E.R3 and E1L all suggest that they were not, at the time of the study, using smart technologies to organise and present instruction in their classrooms. We infer from these responses that there could be a need for interventional studies to investigate and increase awareness of the use of STAI in higher education across developing countries such as those on which we focussed in our study.

In another set of responses, E14L, E36N, E54N, E17L, E29R and E11R all identified plagiarism detector software (Turnitin) as a smart technology that can be used to detect plagiarised work in students’ online assignments and examination submissions. In agreement with the position on STAI expressed in the responses of these six participants,, the researchers identified plagiarism detection software as a tool that can be beneficially used to appreciate teaching and examination integrity and to encourage students in higher education institutions to submit credible and authentic work. In addition, the authors collectively agree with the response of these students in the way it aligns with Vygotsky’s ideology on the use of tools, in this case technology, to advance knowledge of teaching and learning concepts. The authors are unanimous in seeing new technology such as plagiarism detectors, particularly Turnitin, as possessing features that encourage automatic and systemic detection of plagiarised academic work, and we see the use of this technological tool as promoting AI in education in many beneficial ways.

While discussing the findings, we noted that the participating educators in the three countries were keeping abreast of those smart technologies which enable the implementation of AI in education. The results showed that educators in higher education systems in Lesotho, Rwanda, and Nigeria are beginning to recognise and value the introduction of smart technology. The results also showed that learning management systems, smart TVs, projectors, internet-enabled devices, and smartphones are being frequently used in higher education across the three countries. However, these three countries differ, in their respective approaches to the use of these smart technologies to support AI in their respective higher education sectors. From participants’ responses it appeared that, in Lesotho and Rwanda, wireless data projectors, smart boards, Quizizz, and Kahoots are rapidly becoming indispensable teaching tools. At the same time, the Nigerian participants indicated that the Nigerian education system needs to speed up the adoption of these diverse technologies in higher education.

The findings are consistent with some of the those in the literature. Ayanwale (2023), Chen (2022), McMurtie (2023), Turugare and Rudhumbu (2020) argue that smart technologies and related technologies are now indispensable instructional aids in the teaching and learning processes of modern higher education in the global context. These authors, in their detailed arguments, promote the view that technologies provide opportunities for self-learning and enhanced pedagogy. We noted, from our findings, that several participants were of the view that there is a need to consider developing alternative plans to identify plagiarised assignments when examinations and assignments are not submitted online, as is currently the case in some higher education institutions in Lesotho, Nigeria, and Rwanda. We also argue that, apart from educators using smart technologies to detect plagiarised assignments, educators are also responsible for reducing students’ unethical use of smart technologies during examinations.

However, based on our findings and on our own experience, we endorse the argument of Rapanta et al. (2021) that one of the post-pandemic challenges in higher education students’ tending to capitalise on using smart technologies to acquire marks they could not practically defend at that academic level. Moreover, the process of identifying plagiarised assignments in hardcopies, and/or unethical use of smart technologies during examination malpractices, may not be an easy task for educators who have not advanced their technological pedagogical skills to measure the technological skills of their students (Ifelebuegu, 2023; Ifijeh et al., 2019; Lim et al., 2023). We argue that educators need to appreciate the extent of their technological pedagogical skills to be appropriately knowledgeable of the functionalities of different smart technologies that can be used for real-time solutions to examination questions.

In addition, educators need to consistently update their technological pedagogical skills and knowledge to ensure that these align with recent technological innovations in teaching (Thurzo et al., 2023). By doing this, they may be aware of certain “smart technological solutions” that students utilise during learning and assessment. This development, we note, needs to be considered in higher education as it could militate against the unethical use of smart technologies which support artificial intelligence in education.

10 Utilising STAI in higher education

The participants’ responses showed the different ways and reasons for using certain smart technologies in instructional practices, and this was useful for extending knowledge on STAI in higher education. From the participants’ responses, we note that they were intending to use smart technologies to create learning opportunities for their students, to accomplish learning objectives through independent and collegial efforts, and to solve teaching tasks quickly and effectively. In other words, these educators were aiming to make the best use of smart technologies to facilitate the kind of learning environment where their students could discuss, demonstrate, and provide answers to subject matter questions (Jammeh et al., 2023; Olugbade et al., 2023).

Reacting to the question of how smart technologies supporting AI were being used by our sampled group of higher education educators to encourage their students to learn in the classroom, E20N responded:

By giving them task to complete that would require the use of technology.

In the same way, E38N exhorted her/his students to use the available technology for learning purposes. Triangulating the Nigerian experience with those of the other two countries, E4L responded “It is good to encourage students to bring technologies to support class assessment”. Similarly, E22L’s response was:

I always tell my students to take advantage of the advent of technology to search for information. I advise them to use their phones to listen to audio books, watch educational videos.

Corroborating these findings, E24R added:

I am just starting to encourage students on how to use them (smart technologies) but with knowledge of citations and references.

Building on E24R’s approach, E22R posited that it is advisable to introduce new tools to students in all in-person classroom sessions with the purpose of encouraging them to use their smart phones in their teaching and learning. Supporting this position, E30L w8rote:

Encouraging my students to embrace the world of smart technologies has always been a crucial aspect of my teaching philosophy. It’s not just about using these tools; it’s about how they can significantly improve your understanding of the subjects we delve into together. First and foremost, I make sure that the smart technologies we integrate are seamlessly woven into our curriculum. Every course and lesson plan is thoughtfully crafted to show you how these tools are not just supplementary but intrinsic to your learning journey. I want you to see their direct relevance and how they can empower you in mastering complex subject matter.

From the foregoing responses, it could be deduced that these educators were making use of smart technologies to encourage their students to conceptualise and develop knowledge of the subject matter (Mhlongo et al., 2023). In the same way, participating educators reported making use of STAI to encourage their students to actively work together to complete the tasks set them in the classroom (Ambarita et al., 2023; Napratilora et al., 2020).

We also observed from the findings that the ways in which participant were using of STAI to encourage their students to acquire knowledge about subjects were broadly similar. One Lesotho educator (E22L), for example, was of the view that STAI should always be used by students to watch instructional films and listen to audiobooks. Two of the participating Rwandan educators, E24R and E22R, agreed that it is beneficial to incorporate STAI into traditional classroom settings to assist and motivate students in their development and their taking ownership of their content knowledge in ways that align with the curriculum. Interestingly, we observed that the viewpoints expressed by the participating educators from the Rwanda and Lesotho higher education institutions on the application of STAI were similar to those of the Nigerian educators, even though the latter group expressed the belief that the application of STAI in higher education needs to catch up with that of the former two nations (as covered in the section on STAI in Higher Education).

11 Challenges of STAI in higher education

While analysing the participants’ thoughts about the role of STAI in teaching processes, we came across some challenges affecting the participants’ use of STAI in higher education. The majority of the challenges appeared to be in the form of obstacles arising from the lack of provision of the kind of facilities that have the capacity and potential to enhance the use of STAI in classrooms and society at large. For instance, E11L emphasised this and the several potential advantages than smart technologies can have when used in teaching and learning:

The usage of smart technologies in education is influenced by various obstacles, some of which can strengthen their implementation, while others may weaken it. These obstacles often depend on factors such as infrastructure, policies, teacher smart technologies have the potential to create more engaging, accessible, and personalized learning environments. They empower educators to make data-driven decisions, enhance collaboration, and provide students with a diverse range of resources and experiences. As technology continues to evolve, its impact on education is likely to expand, opening up new opportunities for both students and educators’ readiness and engagement.

In support of E11L’s emphasis, E21R, E18L, and E42N recounted their experiences of the various obstacles. They identified poor Internet connectivity and some students’ inability to afford modern technologies which support teaching and learning as two of the current obstacles militating against the use of STAI in educators’ professional development.

E22N identified a lack of funds and an inadequate supply of technology as obstacles delimiting the use of STAI in classroom practices. Resonating with the responses of E22N, E21R and E18L, E2L and E13N saw weak Internet connection and teachers’ lack of technological skills as either strengthening or weakening their use of smart technologies in education.

Adding to these responses, E7N argued that poor policy in terms of providing Information Communication Technology facilities hinders the implementation of STAI in classroom instructional practices. E12R, E30R, and E53N expressed a different view of the obstacles. They identified poverty, lack of electricity, fear, ignorance and technology complexities as both intrinsic and extrinsic factors affecting the use of STAI in professional development.

While analysing these findings, and comparing them with those from similar studies in the literature, we noted that the sampled groups of educators in the three countries in our study were becoming aware of intentional use of STAI when designing lessons and to improve their knowledge of, and skills in, teaching, similar to the participants in other studies (Alam, 2022; Ayanwale, 2023; Masabo et al., 2023; Olugbade et al., 2023; Jammeh et al., 2023). We also discovered that the participants had been able to identify various social, economic, and institutional factors militating against the use of STAI in higher education, similar to those found in studies done by Ayanwal (2023), Masabo et al. (2023), Mhlongo et al. (2023) and Turugare and Rudhumbu (2020).

We also noted a pattern of convergence in the viewpoints expressed by the participants regarding the obstacles impeding the application of STAI in higher education. Participants from the three countries were broadly in agreement that inadequate funding and training policies, a lack of infrastructure, erratic electricity supply, poor Internet connection, and a lack of the kind of technical expertise for educators to be able to take advantage of STAI’s features could impact the application of AI in higher education. However, we argue that, notwithstanding these obstacles as perceived and experienced by the educators in our study, their – and other higher education educators’- decisions about, and efforts to, apply STAI in professional teaching and learning activities can deepen their understandings of “what” specific factors support or limit the use of STAI in the classroom. Within this context, we were able to argue that educators might become familiar with the challenges over time and be able to both enumerate and, in so doing, go some way to overcoming, them.

12 Discussion

As discussed earlier, participants in this study were educators teaching in Nigerian, Lesotho and Rwandan higher education institutions. Their viewpoints expressed in their responses to questions in a semi-structured questionnaire provided the data that answered the research question: What are the viewpoints of a sampled group of Lesotho, Rwandan and Nigerian Educators on Smart Technologies Supporting AI in Higher Education?

All participants attested to the importance of STAI in professional development. They had been involved in the use of any Learning Management System, ChatGPT, Cloud Computing, Smart TV, Interactive Whiteboards, Chatbox, Internet-Enabled Tablets, smartphones, smart boards, Kahoot, and Quizizz to plan, teach and assess their lessons in higher education. Drawing from the responses provided, we deduced that the participants were, at the time of the study, employing all of the identified smart technologies to initiate communication in the classroom, gain subject matter knowledge, expertise and receive feedback from student on the lessons they had presented.

Most significantly, the results suggest that the participants were advancing their teaching strategies to match the standard of educators in industrialised nations by utilising all of the aforementioned smart technologies which support AI, which in turn supports a constructivist learning environment. This is a learning environment characterised by collaborative learning, sharing of learning experiences, candid discussion about questions and suggestions, and the use of technology as a knowledge-mediating tool (Churcher et al., 2014; Theodorio, 2024). In addition to these teaching and learning strategies, we observed from the participants’ viewpoints that they saw smart technologies which support AI as a tool that helps implement synchronous and asynchronous teaching methods, and as a tool that inspires students to actively participate in learning activities. Educators’ teaching philosophy and use of STAI are built on active participation in the classroom, which involves using technology to acquire knowledge of subject matter content and collective interaction among students to share knowledge of subject matter (Chen, 2022; Dewi et al., 2021; Larassati & Rachmadiarti, 2021).

Furthermore, the data collected using the semi-structured questionnaires helped educators communicate their pedagogical experiences by applying a range of STAI tools in higher education. The viewpoints provided by the participants gave an understanding that collaborative learning of concepts in the classroom requires the use of ‘cultural tools’ - such as STAI to create a collective development of subject matter knowledge. Additionally, since the research approach, with participants responding to several open-ended questions, followed a qualitative and interpretive method, educators were open to the opportunity to describe the ways in which they had encouraged their students to adopt STAI for learning, both in- and outside the classroom. We argue that, by encouraging their students to utilise STAI to support their learning of concepts, these participants and their students had at least begun to acquire and to use these technological skills and knowledge in their various learning and teaching contexts.

Based on our findings, we recommend that new studies be conducted to investigate the specific reasons why some educators do not use smart technologies in the teaching process. This phenomenon emerged from the data gathered: some of the participating educators indicated that they were not using smart technologies for teaching purposes. However, in the next stage of this study, we hope, in the process, to engage with educators’ country-wide, in order to identify and demonstrate the use of STAI in higher education teaching processes. We anticipate that this procedure may serve to help educators become familiar with, and knowledgeable about, STAI and its use in their classrooms and help comprehend the theoretical implications and technological paradoxes of employing STAI both in higher education and for their professional and personal growth.

Furthermore, we envisage that all educators and institutional policymakers, particularly in developing countries, may understand the need to prioritise technological affordances and constraints in the choice and use of STAI in pedagogical plans. We also argue that, with proper planning, policy, training, and provision of smart technologies, the use of artificial intelligence in education will become a reality for both educators and students, STAI use in higher education in developing countries like Nigeria, Rwanda and Lesotho may become equal to, or commensurate with, current practice in higher education institutions in the global North.

13 Concluding remarks

According to the findings of our study, and the views of our respondents, the introduction of smart technologies into teaching and learning activities in higher education institutions in Nigeria, Lesotho and Rwanda, respectively, has both encouraged and advanced the use of AI in education in these countries, although to a limited extent, due to various obstacles. Obstacles such as poor electricity, lack of provision of the kind of facilities that have the capacity and potential to enhance the use of STAI in classrooms and society at large, poor funding and poor training arrangement militate against the use of STAI in the three nations’ higher education institutions. It has also encouraged the sampled group of higher education educators from these countries to rethink the pedagogy they have been using in the process of their professional development. Additionally, Rajeb et al.‘s (2024) study on the effects of artificial intelligence (AI) on education and Bond’s (2020) study on student engagement through flipped learning have revealed that the usage of STAI in higher education has the potential to affect teaching methodologies. These (STAI) have the ability to influence the development of students’ cognitive skills, heighten their interest and motivation toward learning, and foster collaborative learning. What emerged from the data was participant having engaged with STAI in their teaching contexts, and being aware of the importance of these technologies in teaching and learning. Although some participants in this study admitted to not using smart technologies for instructional practices, often due to the existence of the obstacles described above, the bigger picture emerging from the data indicates that some participants possessed varied levels of technological knowledge in the use of STAI to plan and assess their instructions. From the findings we argue that what is needed is to increase awareness of AI amongst all educators in higher education in the three countries to serve as encouragement to both faculty members and students to use STAI for learning purposes. In so doing, those obstacles needing critical attention in the use of STAI in higher education might begin to be addressed or even overcome.