Teachers in the Age of Artificial Intelligence: Preparation and Response to Challenges

Abstract

In the field of higher education, artificial intelligence (AI) stands as a transformative axis, presenting challenges and opportunities for both teachers and students. This chapter profiles the innovative teacher, whose responsibilities expand toward guaranteeing quality education that is adaptive to current technological demands. Students’ digital competence is critically examined, identifying the gap that exists when entering higher education and focusing on the ethical and practical challenges inherent in the use of AI. The importance of continuous teacher training and strategies that promote active involvement in AI is emphasized, to cultivate a deep understanding and effective application of these technologies in the educational process. It is recognized that AI can provide personalized and self-regulated learning, but it does not replace the essence of human mentoring, essential for its capacity for empathy and creativity.

1. Introduction

The question of the ability of machines to think was first raised in 1950 by Alan Turing [1]. Based on this, the theoretical foundations were established to develop a technology that was capable of imitating human thinking. With this background, the term Artificial Intelligence (AI) was first raised at the Dartmouth Founding Conference in 1956 by John McCarthy [2]. AI is a technology that is made up of algorithms that run continuously to predict and complete complex tasks in seconds. This sequencing algorithm takes back information learned through the machine learning (ML) process [3]. Machine learning is defined as “a subfield of artificial intelligence that includes software capable of recognizing patterns, making predictions, and applying the newly discovered patterns to situations that were not included or covered by its initial design” [4]. In education, main elements of AI are machine learning, educational data mining, and learning analytics [5]. UNESCO defines Artificial Intelligence as “systems capable of processing data and information in a way that resembles intelligent behavior, and generally encompasses aspects of reasoning, learning, perception, prediction, planning or control” [6].

The focus on AI as an element of interest for society accelerated in 2023 with the emergence of an update to the ChatGPT application from the company OpenAi [7, 8, 9]. This application made it possible to integrate with other applications and digital technologies, which led to greater accessibility to AI for the population. Several economic and social sectors have begun their adapt to the use of this advanced digital technology as a basic and daily element of the activities demanded by their sector [10].

The educational field has not been the exception in the adoption of AI, since it provides the facilities for personalized training and self-regulated learning. A study from the United States of America reported that 38% of students use some AI at least once a month [11]. A global survey identified that 50% of university students, globally, have used AI to study and typically enter one or two questions a day [12]. As for teachers, other surveys identified the importance of training them on AI topics. The most notable results show that 65% should be trained to integrate it into their teaching methods, 70% think that teachers should teach students how to properly use AI and another important element is related to the ethical implications of the learning process, since 71% of people agree that teachers should receive training to identify the originality of educational tasks, such as essays, among other activities [13]. These results give indications that we are beginning a new educational era, that of Artificial Intelligence.

Due to the multiple challenges faced by the integration of AI in higher education, in this chapter, we want to reflect on the contributions of Artificial Intelligence in Education (AIEd) and the challenges they pose for teachers. Additionally, we want to talk about the ethical dilemma that this technology presents in the educational field.

2. Artificial Intelligence in education

Although we are facing a new era, interest in integrating Artificial Intelligence for educational uses has developed since the end of the last century. In 1993, to help with the advancement of knowledge and the promotion of research in the field of Artificial Intelligence in education, The International Society of Artificial Intelligence in Education (IAIED) [14] was created, which in turn time publishes a magazine related to the subject [15].

A meta-systematic review identified six main benefits of the integration of AIEd: personalisade learning; greater insight into student understanding; positive influence on learning outcomes; reduced planning and administration time for teachers; greater equity in education and precise assessment & feedback [16].

Likewise, four AIEd typologies have been identified: profiling and prediction, intelligent tutoring systems (ITS), assessment and evaluation, and adaptive systems and personalization [15]. In turn, these have given guidance on the classification of the application of AI in education (Figure 1) [16].

Figure 1.

AIEd applications. Source: Based on Bond et al. 2014 [16].

Profiling and prediction in the use of data collected by AI to provide information that helps in forecasting the possible academic trajectory of students. Likewise, it provides a diagnosis that will guide the decision-making of the senior teacher to design effective teaching strategies that improve academic performance. It provides the administrative body of the universities with the elements to identify the student retention route and avoid abandonment. Another of its virtues is that it offers a vision of the areas where the student will need support [15, 16].

Intelligent tutoring systems (ITS) help students with self-regulated and personalized learning, giving them tools that allow them to enrich their learning experiences. ITS provide them with immediate, automated feedback. Among the benefits of these systems is that they help recommend teaching materials that fit the needs of the student. In addition, it makes it easier for them to collaborate with their peers. They provide teachers with the information necessary to diagnose students’ learning needs and strengths [15].

Assessment and evaluation help educational institutions evaluate students, their understanding of content, and acquisition of skills and provides automated qualifications and online exams, among others. It also allows evaluating the quality of teaching and evaluates teachers and the effectiveness of the learning methodologies used [15, 16].

Adaptive systems and personalization contribute to personalized student learning. They guide them on different learning routes that fit their needs. They contribute to self-regulated learning and facilitate a deeper understanding of the knowledge they wish to acquire. They help teachers improve their teaching process through recommendations for the design of a pedagogical route, and they also help them monitor students [15, 16].

For example, chatbots can provide study guides tailored to learning needs and personalized educational resources [17]. Research showed that ChatGPT support for students who had self-regulated learning experiences performed well in activities related to the planning and facilitation of educational materials; however, it was not shown to be sufficiently appropriate for an evaluation that provides effective feedback [9].

The literature on the subject shows that human tutoring is more effective than that assisted by AI since it offers human qualities such as emotional support, intuition, compassion, empathy, and creativity [18, 19]. This makes it easier for students to obtain highly informative feedback that allows them to improve and acquire the expected learning results [20], which, when effective, helps improve motivation, self-regulation, self-efficacy, and metacognition [21]. Some studies have identified that students positively value the soft or socio-emotional skills of their teachers. These emotions provide elements in favor of not replacing the university professor with AI [22].

3. Challenges in the application of AI in higher education

Despite the imminent impact of AI on teaching and learning in higher education, a UNESCO survey revealed that less than 10% of universities have formal guidance on the use of AI [23]. This shows that, although AI offers great opportunities to enhance learning, its integration into the educational system is still beginning. This represents various challenges for pedagogical agents such as teachers.

A systematic review identified the advantages that AI can bring to teachers in the educational field. To do this, it takes up three moments of the pedagogical act: Planning, Implementation, and Evaluation (Figure 2) [24].

Figure 2.

AI advantages for university teaching. Source: Based on Celik et al. 2022 [24].

However, these advantages it offers also come with challenges. In this section, we highlight the limitations in teachers’ preparation to integrate AI into teaching in ways that foster meaningful and situated learning for their students. Teachers are the main educational actors for improving educational quality [25]. For this reason, the training and preparation of university teachers in IAEd requires proposals that provide solid foundations on how to integrate it into the pedagogical act. In the literature, we identified some AI training proposals that consist of three stages: understanding AI, using AI tools, and experiencing AI programs [26].

In the first stage, teachers are intended to understand concepts related to AI, such as, for example, automated learning and how it works. Once this theoretical knowledge is obtained, it is expected that they will be more receptive to applying it for educational purposes. This will help them increase their confidence and overcome the algorithm aversion effect, characterized by the loss of credibility in automated recommendation systems after making an error, and instead tolerating the same error when it is made by other people [27]. Overcoming this bias will make them understand that AI has limited capacity for some activities but still provides tools that can be leveraged to apply in teaching.

Furthermore, we consider that at this time, some myths related to educational technology must also be analyzed and overcome, such as pedagogical materialism, which defends that digital technologies are the only educational materials available on the Internet added to teaching [28].

The second stage is core; this seeks to encourage the teacher to explore and experiment with different AI tools to learn about their functions and usefulness for teaching. The importance of this stage is related to overcoming the three elements of the Technology Acceptance Model (TAM), which are perceived ease of use, perceived usefulness, and attitude toward use [29, 30].

A study identified that teachers’ perception of risk [31] and feelings of anxiety [29] negatively influence the adoption of AI in education. That is why, at this stage, teachers are encouraged to develop a positive attitude about the use of AI through experimentation with different applications and tools; this will help them increase their confidence. Practice will help them reduce their anxiety and reduce the feeling of risk.

In the third stage, experimentation with different AI programs is interesting. These types of experiences will reaffirm knowledge about the uses and limitations of this technology in various tasks or activities. At this stage, it is considered necessary for teachers to return to the three elements of reflective practice: (1) collect data, (2) data analysis and evaluation, and (3) triangulation of information [32]. The integration of these three elements will help them with key moments of the pedagogical act, such as planning, implementing, and evaluating the educational task efficiently.

Data collection is important because it guides the teacher about what is happening in student learning, for example, learning styles, the impact of the strategies used, time use. For the second and third elements, teachers must have the basic knowledge to interpret AI-based learning analytics [27]; this skill will help them make more effective decisions for teaching.

4. Ethical and practical challenges of AI in education

To obtain data that feeds AI machine learning (ML), the applications must have confidential information from students and teachers, which represents an ethical dilemma for their use [15].

The lack of security and critical thinking about information shared through AI poses great risks for students, teachers, and educational institutions. Access to digital environments and advanced technologies does not guarantee that current and future students are competent in the use of technology [28, 33]. Despite being immersed in it since its birth, its effectiveness in safely using different technological tools has been questioned. It has been found that they are more likely to share personal and confidential information in different applications without investigating the possible consequences on their security and privacy [34].

These ethical dilemmas regarding the use of confidential and private data of students and teachers for educational purposes are not recent [35, 36]. For this reason, it is an issue that is on different political agendas to impose measures that guarantee the security and privacy of information [5]. For this reason, guidelines have emerged that allow the implementation of a safe, fair, responsible, transparent AI, guaranteeing the privacy and data protection [37, 38]. In the educational field, this concern has been shared by organizations such as UNESCO, which demands the creation of robust policies to ensure that advances in AI do not compromise privacy or exacerbate existing inequalities [39].

Researchers and educators must work together to ensure the responsible use of AI. This includes the development of regulations and policies that oversee its use, in line with the laws of each country.

5. Teaching competencies to integrate AI

Faced with the above, we ask ourselves: What are the key competencies that university teachers must develop in order to integrate AI efficiently into the teaching and learning process? Within the whole range of response possibilities, we consider digital teaching skills and socio-emotional skills to be fundamental. Digital teaching skills due to its relationship with the pedagogical use of digital technologies; and socio-emotional skills due to the need for humanization and management of emotions in the use of AI in education.

Teachers’ digital competencies are the set of knowledge, skills, and abilities of teachers to provide solutions to the different challenges presented in their educational actions from an ethical, safe, and responsible point of view in synergy with the use of digital technologies and tools in the teaching and learning process.

It has been shown that one aspect that benefits teachers’ self-efficacy is emotional intelligence. This is an individual’s ability to understand and regulate their emotions [40, 41]. Accordingly, social-emotional skills are the “... a set of skills that allow us to manage our own feelings and emotions and those of others, with the intention of guiding thought and actions towards satisfactory performance, dealing in the best way with different emotional states as a way of motivation and meaning in life...” [41].

Considering that emotional intelligence and emotional skills are fundamental for teaching efficiency, there is a need to put them into practice in educational settings. This is why the need arises for the development of socio-emotional competencies, considered “Emotional awareness”; acquiring the ability to use and regulate them in different social contexts and educational environments [42].

Due to the predominance of digital technologies in education, the construct of digital emotional competence emerged, which is a fusion of digital competencies with emotional intelligence [43, 44].

Therefore, any educational activity in which AI is used must have a strong human component that supports the regulation and management of the emotions of the educational actors involved.

In addition, it is considered pertinent for higher education institutions to manage the incorporation of the aforementioned competencies for teacher education and training activities.

6. Conclusion

Exposure to technology does not guarantee the development of digital competencies; oftentimes, we possess intelligent technological resources, and their full potential is not exploited. It is necessary to take a critical and comprehensive position on these new generations, based on designing educational proposals that promote and evaluate the development of these skills.

Promoting critical thinking is crucial in a landscape where technology infiltrates all aspects of teaching. AI, although it is a powerful tool, must be the subject of detailed analysis for its effective incorporation into the educational environment. Educators must cultivate this quality, encouraging students to critically analyze and question both the information and the technological solutions they encounter.

For these methods to be effective, educators must be willing to acquire new competencies and keep up to date with technological and pedagogical developments. This includes participating in professional development programs and collaborating interdisciplinary to integrate AI into the classroom to enrich the learning experience. The creation of communities of practice and professional support networks will be essential for the exchange of ideas and strategies that facilitate the effective adoption of AI in teaching.

Understanding teacher professional development as a dynamic and adaptable process, rather than as a static requirement, is vital. By prioritizing continuous training, teachers can be prepared to lead the era of AI in education.

7. Key questions and reflection

Given the above, we ask ourselves: What are the key competencies that university teachers must develop to integrate AI efficiently into the teaching and learning process?

Conflict of interests

The authors declare that they have no conflicts of interest.

Notes/thanks/other declarations

We would like to thank the staff of the Ministry of Education of Science and Technology of El Salvador for their constant effort and commitment from their different workplaces. Thank you for all your support in the various programs and strategies for the implementation of digital innovation and for maintaining the quality of education at all levels. We also recognize and thank each teacher for their commitment to the digital transformation of the educational community.

Appendices and nomenclature

AI	Artificial Intelligence
AIEd	Artificial Intelligence in Education