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Review Article | | Peer-Reviewed

AI Literacy for Teacher Development: Challenges and Pathways in Developing and Underdeveloped Contexts

Mst. Anjona Khatun,  Md. Saddam Hossain*,  Tashikuzzaman,  Sumaya Sumi
Published in Teacher Education and Curriculum Studies (Volume 11, Issue 1)
Received: 6 March 2026     Accepted: 20 March 2026     Published: 30 March 2026
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Abstract

Artificial Intelligence (AI) gradually alters educational practice, presenting new demands to the pedagogical, ethical, and professional skills of teachers. However, the overall research about AI literacy and its role in teacher development is predominantly focused on the developed context, thus limiting our understanding of the challenges facing such teachers in developing and underdeveloped societies. To fill this gap, this paper conducts a critical thematic review of empirical studies, conceptual literature, and policy-focused studies published since 2019. The review puts together key challenges, including infrastructural barriers, limited accessibility to continuous professional development, the lack of pedagogical preparation, ethical and equity challenges, and inadequate policies. In addition to this, it outlines new directions, such as context-specific professional development models, scalable virtual learning platforms, equity-focused frameworks, and human-AI collaborative strategies. The review redefines teacher development as a kind of professional agency operating on the concept of AI literacy as the convergence of technical knowledge, pedagogical acuity, and critical ethical awareness. The study also emphasizes the importance of strengthening institutional support, policy coordination, and collaborative professional learning environments to foster sustainable AI literacy development among teachers. The results offer practical lessons on enhancing equitable, sustainable, and context-based AI adoption in contemporary education systems globally.

Published in Teacher Education and Curriculum Studies (Volume 11, Issue 1)
DOI 10.11648/j.tecs.20261101.14
Page(s) 31-41
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2026. Published by Science Publishing Group
Previous article
Keywords

AI Literacy, Teacher Professional Development, Generative AI, Educational Equity, Developing Contexts

1. Introduction
The recent enhancement of artificial intelligence (AI) in education is altering the pedagogical approach because it allows customizing learning, automating the routine teaching and assessment processes, and improving the teaching and evaluation process. AI-based systems are supporting an adaptive feedback, learning analytics and intelligent tutoring with greater frequency, thus redefining the role of teachers and instructional decision-making. Although these advancements promise a lot of potential in the field of educational innovation, they also place new requirements on teachers regarding their professional knowledge, skills, and ethical judgment, especially regarding the responsible introduction of AI technologies into the classroom
[1, 8]
.
However, although AI has a global realization wave, its advantages are not evenly spread. There is still a strong digital and AI disparity between developed and developing or under-developed areas, and educators often have to face infrastructural constraints, a lack of access to digital resources, and a lack of professional training. Under those circumstances inequities in connectivity, institutional support and policy implementation restrict the capacity of teachers to be purposeful in their involvement with AI tools, a situation that leads to superficial or scale-effected adoption. These systemic imbalances have the potential to strengthen the current disparities in education instead of relying on AI as an inclusion and pedagogical tool
[1, 5]
.
In this landscape, since AI literacy is a vital component of teacher development, it has become incredibly important. The concept of AI literacy can be perceived as a set of knowledge, skills and attitudes that allow an educator to gain familiarity with the way AI systems work, pedagogically use AI tools, and be able to critically analyze their ethical, social, and educational consequences. In terms of teachers, AI literacy helps in informed design of instructions, interdisciplinary learning, and ethical decision-making, besides training learners to be part of AI-based economies and workplaces
[8, 26]
. In this regard, AI literacy is not confined to technical skills, but it is rather a practice that has critical awareness, professional agency, and equity focus.
Nonetheless, the learning of AI literacy among teachers is still associated with challenges. According to existing research, gaps in training teachers, ambiguity with curriculum integration, and unsolved ethical issues associated with the bias of algorithms and data privacy and responsibility can be identified. They are especially sharp in the resource-limited settings, where professional development opportunities tend to be intermittent and externally instigated, and where the frameworks of AI application in education are still in their immaturity
[8, 21]
. Educators in such places will be left out in the initiation of AI-enabled learning reform intent unless solutions to the education process experience are organized and context-sensitive.
To cope with them, teachers, policy makers and other interested individuals should work together by coming up with holistic competency frameworks and policies of equity of access. The former research is concerned with the need to align AIs literacy programs to broader professional development systems, and with infusing moral and governance issues into policy and practice
[6, 8]
. The application of AI literacy in under-developed and developmental environments may be scaled down through purposeful investments in the digital environments, education of the instructors, and incorporation of the curriculum to ensure that the adoption takes a sustainable form
[1, 24]
.
The emerging information also shows that an increase in an AI literacy of teachers may produce greater transformational pedagogy. Teachers who are AI literate are more likely to establish genuine learning environments which promote critical thinking, problem solving, participation of learners. These behaviors have been associated with the improvement in the performance of the students and more significant integration of technology in instruction and learning
[17, 19]
. In a more general sense, the power to concentrate on AI literacy in the professional growth of teachers is being viewed as the priority in the utilization of AI potential enhancing equity and innovation of education, where specialists are informed educators, yet not obedient servants of intelligent frameworks
[13, 16]
.
It is on this context that the present review critically examines the issue and directions associated with issues of developing AI among teachers in developing and underdeveloped contexts. The present review aims at synthesizing the current research on empirical studies, conceptual literature as well as policy-oriented literature to bring forth some light on how AI literacy may be utilized in supporting equitable and sustainable teacher professional growth in the circumstances of resource limitation.
The review area is based on infrastructural, pedagogical, ethical, and policy aspects of AI literacy with special focus on instructional design and professional learning models.
Accordingly, the review is guided by the following objectives:
1) To examine how AI literacy is conceptualized within contemporary literature on teacher professional development;
2) To identify key challenges limiting teachers’ access to AI literacy in developing and under-developed contexts;
3) To synthesize emerging pathways and practices that support context-sensitive, scalable, and equity-oriented AI literacy development;
4) To outline implications for instructional design, policy formulation, and future research.
The framing of AI literacy as technical proficiency and critical consciousness implies that this review will be relevant to the current discussions of equity and innovation in education in the age of AI. It establishes teacher AI literacy as one of the keystones to the empowerment of professional agency, pedagogical quality, and inclusive and transformational education, especially in the sphere of structural and resource-limited contexts.
To meet the aims, the review develops a conceptual and theoretical background of the knowledge of AI literacy and teacher development and then analyses the challenges and pathways that are identified in literature.
2. Conceptual and Theoretical Foundations
2.1. Defining AI Literacy: Technical Competence and Critical Awareness
Like the idea of AI literacy is gradually turning into a multidimensional concept as it does not just presuppose a certain level of technical ability, but also the critical perception and ethical thinking. Rather than focusing on the ability to use AI tools, contemporary literature has a tendency to emphasize the importance of reflective engagement with AI technologies and their socio-cultural implications. In this respect, AI literacy enables not just teachers to use AI systems in classrooms but also enable them to be in a position to question their assumptions, limits, and the impact they are likely to have on students and the society.
This holistic understanding of the AI literacy is justified by many frameworks. Tofiq and Latif observe that in their conceptualization of AI literacy, they are able to engage with AI-based technology socio-culturally mindful, ethical and critical
[25]
. Similarly, through the OECD Education 2030 framework, Mima conceives AI literacy as the skillful composition of knowledge, skills, attitudes, and values that will guarantee responsible AI usage and proper engagement with the society
[22]
. Choi et al. also explain AI literacy in terms of knowledge-based, skill-based and ethical-attitudinal aspects, highlighting the importance of interdisciplinary education and critical analysis of such aspects as prejudice and data security
[8]
.
Viewpoints that are based on education also reinforce dispositions and interactional competencies. Chiu et al. emphasize the role of confidence and self-reflective attitudes in AI literacy in school
[7]
, whereas Long and Magerko also point to the competence of successful human-AI interaction with the help of learner-centered design
[19]
. In addition to each other, these perspectives project AI literacy within the context of integrating technical competence and critical awareness. The review conceptualization of AI literacy therefore means background ability that enables teachers to utilize AI tools and undertake ethical judgment and informed decision-making in as far as making choices regarding the design of instruction, in particular, in education with limited resources.
2.2. Teacher Professional Development (TPD): Models, Purposes and Trends
The systematic and ongoing actions during which teachers expand their professional knowledge, pedagogical actions and teaching practices are referred to as Teacher Professional Development (TPD). The latest studies are gradually converging to the opinion that TPD is a continuous, practice-based, and situation-specific endeavor rather than a sequence of personal training actions. However, the centrally given and standard workshop has been harshly criticized as inflexible and unrealistic to the classroom environment particularly in a high-paced technological environment
[14, 18]
.
More dynamic TPD models which pay attention to school-based learning, collaboration as well as integration of online and field-based professional knowledge have emerged in order to counter these restrictions.
The hybrid forms of TPD, networked learning community and demand-driven professional learning model, are examples of the move towards meaning-making TPD that allows continuous professional inquiry and reflecting practice
[15]
. This transition also gained momentum with the rapid growth of online platforms amidst the COVID-19 pandemic signaling forth the technology-mediated models of professional learning, individualized growth paths, and collaboration among peers as the main elements of the successful TPD
[4]
.
Trends in the field of TPD are now more focusing on collaborative and collegial learning settings that facilitate instructional enhancement, classroom organization, and subject-area proficiency. These strategies acknowledge teachers as participants in their own professional development and not as recipients of training forced on them. Notably, TPD is currently seen as a systemic and collective process relying on favorable policy conditions, sufficient resources, and multi-stakeholder involvement
[10, 12]
.
Against the background of AI implementation in teaching, these changes highlight the incompetency of the traditional, workshop-based TPD models. Pedagogical change facilitated by AI necessitates teaching professionals to adjust their work continuously, critically assess new technologies, and gain new instructional design competency. In this way, the AI-driven TPD should be iterative and technology-based and should be tightly linked to the daily practice of teachers. This redefinition of TPD offers a critical basis upon which the usefulness of AI literacy may be effectively integrated into teacher professional development, and notably in non-advanced and less-advanced settings.
2.3. Theoretical Lenses
This review is guided by three complementary theoretical perspectives, which include equity and access in education, technology integration and instructional design, and professional learning in virtual and adaptive systems. Taken, all the lenses provide a cohesive analytical approach to how to think about the issue of AI literacy as a pedagogical, ethical, and structural issue in terms of teacher professional development, particularly in developing and under-developed contexts.
2.3.1. The Discussion on Equity and Access in Education Moves to the Second Point
AI literacy does not qualify as a value-neutral phenomenon: it exists as in an existing system of power and access that determines who and under what circumstances educational technologies may be used. Equity Technological devices have the capability of reducing educational inequality because they can offer personalized and inclusive learning opportunities, namely by using adaptive learning systems that may be customized to the needs of different learners
[9, 20]
.
Nevertheless, unequal access to digital infrastructure, network access, and institutional coverage remains a marginalization to teachers and learners in underprivileged situations. Consequently, AI literacy needs to be perceived as an equity problem, which needs to be addressed not only at the individual level but also at the system level, in terms of both infrastructural investments and comprehensive digital policies
[2, 11]
. This is why this lens is tied to the emphasis on developing and under-developed settings in the review, where structural constraints largely determine the opportunities of teachers to interact with AI.
2.3.2. Technology Integration and Instructional Design
In terms of instructional design, AI literacy defines the ability of a teacher to design, adjust and assess AI-based learning eco-systems. Technology integration is not only about the adoption of the tools but a process of matching the digital technologies to the pedagogical objectives to create critical thinking, creativity and meaningful learning experiences. A successful design of instructions employs AI-enabled tools to support the satisfaction of various learning needs and advance equitable access to learning resources, therefore, expanding the pedagogical repertoires of teachers
[11, 23]
. This prism anticipates AI literacy as an instructional decision-making competence and not a technical ability.
2.3.3. Professional Learning in Virtual and Adaptive Systems
The prism of professional learning of virtual and adaptive systems demonstrates that technology-mediated environments are important factors in promoting scalable and flexible teacher development. Online and adaptive platforms allow ongoing interactive and context-driven professional learning that is especially essential when resources are limited to a setting with an absence of access to standard training. These systems assist the educator to build the competencies needed to effectively integrate the emerging technologies with encouraging reflective and equity-focused practice
[3, 27]
. This prism supports the focus of the review on the online and adaptive pathways on developing AI literacy and the analytical preconditions on locating scalable professional development models.
The combinations of these theoretical lenses provide the AI literacy theories as a multidimensional construct that is manipulated by the equity issues, pedagogical design choices, and professional learning systems. They lead the thematic synthesis of the issues and directions that follow in the review.
2.4. Global North–South Divide: Digital Inequities and Implications
Structural differences in digital infrastructure, policy capacity and institutional support rather than ideological differences are the primary factors in determining the Global North-South divide in education. The adoption of advanced educational technologies is limited by factors in most developing and under-developed settings due to limited connectivity, poor access to digital tools, and disjointed policy in most of them. These structural circumstances directly affect the level of the integration of AI-enabled tools into the teaching and learning practices. Until the AI-related innovations in education become even more widespread than they are now, as Adil along with Cabral and Palavras show, the implementation of AI-related educational innovations in less-developed settings continues to be uneven and in many cases, insufficiently scaled because of systemic capacity issues
[1, 5]
. As a result, the formation of AI literacy depends, among other factors, on both personal competence and the existence of favorable technological and policy conditions.
These structural inequalities come with far implicating consequences on the professional status of teachers and learning. In conditions of the scarcity of digital infrastructure and institutional facilitation, educators are most likely to be poorly provided with AI tools, demonstrating insufficient professional development, and having no adequate exposure to pedagogically relevant applications of emerging technologies. This forms a form of professional marginalization where teachers are being reduced to being a passive consumer of technologies designed outside of them rather than being agents in AI-mediated pedagogical innovation. According to Guo and Yu, in such situations, teachers can hardly become AI literate and make professional judgment in the creation of the instructions
[13]
. In order to address the Global North-South divide, the next step would be to perceive AI literacy as a systems issue, which can be addressed by structural constraints and emphasis on potential capacity-building steps that can cause the teacher in the resource-strained environments to play an active role in the process of AI-driven change in education.
Together, all these theoretical explanations and conceptual perceptions complete the pattern of analysis of the review and result in the theme synthesis of problems and possibilities of AI literacy in teacher professional development that the following sections would address.
3. Methodology
On the above conceptual grounds, the paper will employ the critical review method in discussing the conceptualization and operationalization of AI literacy during teacher professional development with the specific reference to developing and under-developing contexts. This is indeed a critical review in the context of the analogic and interdisciplinary concept of AI literacy that enables analytic and not exhaustive synthesis to be performed. The review is based on empirical research, conceptual articles and policy papers published since 2019 and in the post-2025 period to reflect the most recent trends in AI-based education.
Peer-reviewed sources and journals on education, technology, policy-related aspects of education were searched as relevant literature, and some chosen conference proceedings and edited volumes were also used. Keywords used in the searches were AI literacy, teacher development, artificial intelligence in education, and educational equity. New and influential publications were given priority based on conceptual frameworks or insights of relevance to the practice.
The inclusion criteria were that the studies had to cover AI literacy or AI-related competencies, target teachers or teacher professional development, and either apply to developing or under-developed settings or be applicable to resource-constrained settings. The studies that were narrowed to the outcomes of students or digital literacy in general without AI reference were excluded.
A thematic synthesis framework was employed to analyze the chosen literature, based on the conceptual background. The results were categorized into two high-level themes namely challenges and pathways to conduct a systematic analysis of hindrances and facilitating factors of AI literacy in teacher development.
Instead of a discrete literature review section, the paper takes a critical thematic review format, where the previous scholarship is conceptualized in the background section and analytically synthesized throughout the results.
The results below summarize the literature reviewed to formulate the most important challenges and directions that define AI literacy in teacher professional development.
4. Findings: Challenges for AI Literacy in Teacher Development
The critical review shows that there is an interdependency complex of issues, which curtails the establishment of AI literacy among teachers in under-developed and developing forces. All these challenges are not cases of individual limitations, but structural, pedagogical, ethical and institutional limitations, which are a complex of which define the possibility of teachers to participate in AI- based practice in education in a significant way in totality.
4.1. Infrastructure and Resource Gaps
The common problem that is present in all the literature is the inadequate digital infrastructure and material resources to apply AI in education. In most developing and underdeveloped settings, the absence of internet connectivity, unavailability of the internet and a lack of technical support among others is also a major hindrance to the teachers being introduced to AI tools and platforms. Such infrastructural gaps do not just debilitate the execution of the classroom but also the possibility of the teachers experimenting, reflecting, and nurturing the AI literacy in their professional practice. Research continuously shows that in the absence of underlying digital infrastructure, AI initiatives related to it are always disjointed and unevenly distributed, limiting their didactic activity
[1, 5]
.
4.2. Limited Access to Professional Development Opportunities
The other significant obstacle is related to limited access to long-term and high-quality opportunities related to professional development with regard to AI literacy. Unless there are resources available, teacher training in relation to new technologies is often patchy, transient and externally imposed, not necessarily integrated in the context of ongoing professional learning systems. These methods do not fit well the fast changing world of AI that demands continuous learning and continuous improvement of skills. Consequently, educators often do not have organized access to technical knowledge as well as critical consciousness of AI to be confident in their ability and capability to integrate AI in a meaningful way into teaching practice
[18, 24]
.
4.3. Pedagogical Readiness and Knowledge Deficits
Besides the problem of access, the literature shows the challenges that are related to pedagogical readiness and knowledge alignment. Teachers although to some extent have been introduced to AI tools tend to struggle with connecting the technologies to pedagogical goals, curriculum building and learner based teaching. This interruption is a reflection of a greater and more enlarged breakage between AI-related competencies and pedagogical insight in which AI is viewed as an appendix and not a element part of teaching format. The lack of knowledge on the possibilities of AI to aid in assessment, differentiation and critical thinking further limits the opportunity of a teacher to make informational judgments about instruction and limit the opportunities of AI literacy to change the educational practice
[8, 26]
.
4.4. Ethical and Equity Concerns: Bias, Fairness, and Inclusivity
It also provokes ethical and equity-based issues and one of the growing problems of AI literacy among educators. Algorithms bias, information privacy, transparency, and appropriateness of AI systems in culture are but some of the issues that have their representatives spouting hale-hardy questions that most teachers would not be ready to answer. Sine of not being critically aware or reasonably able to analyze AI tools in a responsible manner or alleviating the possibility that the technology would impact vulnerable learners, could also happen without explicit moral guidelines and specific training. The given concerns are particularly applicable in low-resource environments, where the already existing educational disparities may be violated by simply blindly applying the applications of AI technologies
[8, 21]
.
4.5. Policy and Institutional Barriers
Weak policy frameworks and a lack of institutional support are as well systematic and further complicates the process of creating AI literacy in teacher development. The review shows that numerous education systems do not have consistent strategies, franchise systems, and curriculum schemes, which specifically refer to AI literacy among teachers. The policies that are available are usually diffusion, being unrelated with the facts of practice and the teachers have been given no direction or institutional support. These openings in governance, in their turn, help in accumulating uncertainty, a lack of equal efforts, and diminishing accountability and contribute to the marginalization of teachers in AI-informed education policies
[6, 13]
.
5. Findings: Pathways and Opportunities
As opposed to structural and pedagogical barriers discussed in the analysis above, the literature reviewed also provides a continuum of novel opportunities that can support the development of AI literacy in professional development of teachers, in particular, in developing and under-developed environments. Those are the avenues that are concerned with the contextual responsiveness, scalability, equity and the preservation of the teacher agency when it comes to the AI-mediated educational change.
5.1. Context-Sensitive Professional Development Models
Another avenue that has been examined as the most coherent one is the implementation of context-sensitive models of professional development that will be able to bring AI literacy initiatives to the educational locale. Instead of importing generic or externally made-up training programs, coming up with feasible plan is prior anticipation of the practice of foreground teachers, curriculum emphasis and constraint of the institution. Locally based and practice-based professional development may also help a teacher to select the AI-related tools and apply them to accomplish culturally and pedagogically significant changes. They base these models on the principles of reflective practice, peer collaboration and gradual learning, and allow one to constantly develop the technical competence and critical awareness
[24, 26]
.
5.2. Virtual and Scalable Training Platforms
Virtual and scalable professional learning systems are a notion that has the potential to be considered a promising means of getting out of infrastructure and access restrictions. Being online, blended and modular types of trainings enable the teacher to participate in the AI literacy building as an asynchronous and scale process without having to make part of the workshops and central training templates. These platforms enable ongoing learning and communication between the group members and resource sharing, so they are highly adequate specifically in geographically dispersed and resource limited settings. The possibility to change accessibility and, consequently, be associated with classroom practice due to the characteristic of virtual platforms pedagogical intent
[4, 27]
.
5.3. Equity-Focused and Inclusive Frameworks
Equity-based models are also another important avenue towards AI literacy in teacher education. The literature points to the necessity to incorporate the values of inclusion, equality, and access in AI literacy programs and make sure that educators become better prepared to identify and cope with algorithmic bias, data privacy issues, and the diverse needs of learners. The equity-oriented strategies establish AI literacy as a tool of alleviating educational inequalities instead of replicating them through inclusive instructional design and the responsible use of technology. These frameworks empower the teachers to become ethical decision-makers and the representatives of the marginalized learners
[2, 11]
.
5.4. Human-AI Collaboration in Teacher Learning
Another way is under the focus of human-AI partnership as a source of teacher learning and professional agency. Instead of portraying AI as a substitute to the expertise of teachers, new thinking places AI tools as the aids in the instructional planning, assessment, and reflective practice
[3]
. With this collaborative orientation, teachers can use AI as an efficiency and an informational tool and preserve the use of professional judgment and pedagogical control. Through AI as a collaborative learning partner, educators may build confidence, critical consciousness, and adaptive knowledge and gain greater AI literacy with experience
[16, 19]
.
5.5. Case Examples from Developing Contexts
Some case studies of developing and under-developed environments also suggest viability of those avenues when applied in the correlations with local possibilities and needs. Among the described programs, the following ones can include specific teacher training interventions involving the introduction of introductory AI concepts, blended models of professional development, in which online materials are used with the help of peer mentoring, and pilot programs facilitated by policy that involved the usage of AI literacy in the existing curriculum. Even though these illustrations remain relative and cannot be said to be proportionate, they denote that a relative degree of AI literacy increase may be realized whenever there is the presence of suitable infrastructure, institutional commitment, and learning environments that are professionally dynamic
[1, 5, 24]
.
6. Discussion
6.1. Synthesis of Challenges and Pathways: Intersections and Tensions
This review demonstrates that the problems and guidelines of AI literacy in professional development of teachers are not discrete and linear. The new opportunities that incorporate scalable digital systems, context-specific solutions of training, and equity-based designs overlap the limits of structure that comprise infrastructural inadequacies, absence of access to continuing professional growth, and collapse of policy circumstances. This intersection offers the reader a general conflict that exists between the selling of AI technologies as revolutionary possibilities presently and the way this influence on education is extrapolated to simply leave it to be dependent on the systems and professional cultures through which it is being applied.
The synthesis shows that technological scalability is an unnecessary sufficient condition not to have any appreciable development of AI literacy. Despite the fact that virtual space and digital technologies require improvement of access, they are based on pedagogic compatibility, moral leadership, and institutionalism. The lack of these conditions means that AI projects are likely to enhance superficial interaction and prevailing inequalities. On the contrary, the avenues found in this review indicate that the development of AI literacy can be feasible when technological affordances are applied alongside professional learning practices, instructional design principles, and equity issues. This result highlights the importance of viewing AI literacy as a relational process instead of a technical one, which involves the system.
6.2. Reframing Teacher Development: From Digital Competence to AI Literacy
The main conceptual input of this review is a redefinition of teacher professional development, where an increase in digital competence is changed to a broader understanding of AI literacy. The conventional digital competence models mainly emphasize the operational competence and the use of tools, which the evidence indicates are inadequate in the educational settings mediated by AI. The AI literacy needs to bring pedagogical judgment and understanding of ethical and social consequences of AI integration to teachers when they interpret output of algorithms.
David reframing puts teachers as active agents in the reshuffle of education with AI applications instead of the customers of technology. The outlined issues, particularly, the ones related to the pedagogical readiness and the issue of moral ambivalence define the limitations of the models of professional development that pay attention to the technical training but overlook the design of instruction and the awareness of the criticality. Conversely, the discussed pathways contribute to the development of the conception of AI literacy as professional agency to empower teachers to adjust AI tools to the curricular objectives, the needs of learners, and the local context. This transformation of digital competence to AI literacy is a substantial repositioning of teacher development in the AI age and offers the abstraction behind the contributions.
6.3. Implications for Virtual Learning and Instructional Design
The findings directly apply to virtual learning and instructional design, particularly to the AI literacy development process among educators. The impact of the AI-powered technologies on the background teaching process, including evaluation, customization, and feedback, also becomes more apparent. Consequently, the growth of AI literacy can neither be disaggregated nor discussed in the absence of teacher capability to design, assess, and optimize learning spaces using technology-based learning. Based on this review, the instruction design should no longer be concerned with integrating AI tools in the teaching process but with intentional pedagogical inclusion that preconditions the aim of learning, inclusiveness, and ethics.
It is believed that online and flexible professional learning spaces are important in terms of forming AI literacy especially in the regions where real-life training is not highly accessible. These settings offer a framework of continuous, dynamic and collaborative learning, in which educators can explore AI literacy throughout time and within their careers. Nevertheless, the synthesis also provides a warning with regard to the use of technology-centered methods, which are aimed at efficiency or automation, and not the depth of the pedagogical process with instructions regarding how to become AI literate, there should be a moderation in the level of technological novelty and stance-taking practice, ethical explanations, and learner-focused values.
6.4. Global Relevance: Lessons for Developing and Developed Contexts
Even though this review pre-announces the expansion and the underdeveloped locations, the insights that are universally relevant across the world are also present. The absence of correspondence between the models of the professional development, the uncertainty of the ethical issues, and the gaps in governance described as the challenges outlined are not peculiar to the systems which are resource-constrained. Instead, they unveil the structural vulnerabilities in more intuitive forms that can be utilized as an informational tool to the more progressive but not as just and pedagogic AI implementation in the developed education systems.
Regarding the developed environments, the results warn against the presence of a pre-supposition of an efficient infrastructure being the sign of a successful AI adoption. The question of teacher agency, ethical capacity and alignment in instruction is focused regardless of resources availability. On the contrary, transposable plans can be provided by the secondary paths in the changing environment, especially, scalable, collaborative, and context-responsive model of professional development, which can be used across other educational systems. Collectively, the discussion validates the point that the promotion of AI literacy among teachers is an issue in all parts of the world and which must be met by equitable, pedagogically responsive, and enabling responses to professions.
6.5. Implications for Language Teaching and Applied Linguistics
This review has direct implications on foreign and second language education. The concept of AI literacy in language teachers does not solely focus on being aware of generative tools, but a skilled usage of these tools in aspects of writing feedback, speaking assessment, vocabulary development, and lesson planning. Practically, AI-enhanced tools in EFL settings are now widely used in the automated feedback of writing, adaptive reading, pronunciation analysis, and simulation of the conversation. Nevertheless, to properly integrate AI generated materials, teachers must also specifically analyze the output of AI generators in the context of linguistic correctness, cultural sensitivity, bias, and correlation with the learning goals of the learning program.
In the case of language teacher education programs, AI literacy should be integrated into both pre-service and in service professional development programs. Instead of introducing AI as an addition to technological skills, programs are supposed to be based on making instructional decisions, ethical evaluation in AI-mediated feedback, and building reflective practices. Scalable virtual professional learning community can in particular in developing contexts offer long-term avenues of enhancing AI competence among language teachers.
The fact that AI literacy is framed as a form of professional agency will allow language teachers to stop being passive consumers of the tools, and become more of the designers of the AI-supported environment of learning. The given shift is needed in case the equitable and circumstances-relevant language education could become better existing, and not eroded by AI integration.
7. Contribution and Significance
In the review, there are three primary scholarly, practice, and policy contributions to the study of AI literacy and teacher professional development.
7.1. Contribution to Scholarship
This review contributes to scholarship by synthesizing recent empirical, conceptual, and policy literature on AI literacy in teacher professional development, with a particular focus on equity-driven challenges and scalable pathways in developing and underdeveloped educational contexts.
The given article seals the literature gap as it predetermined AI literacy in teacher professional development within the developing and underdeveloped settings that can never be reflected in the literature. The equity viewpoint makes it possible to synthesize the empirical, conceptual, and policy-based research that proceeds with the multi-dimensional conceptualization of the AI literacy as the combination of the technical competency, the pedagogical judgment and ethical consciousness.
7.2. Contribution to Practice
Regarding teacher educators and training agencies, the review is a scale of analytically based information regarding the low quality of the old fashioned workshop-based professional development model and suggests another alternative, which is context-specific and up-scaling. The outlined directions can serve as an effective guide to how the professional learning programs that will contribute to maintaining the engagement, agency, and equitable administration of instruction can be designed in the AI-mediated learning environment.
7.3. Contribution to Policy
In the policy level in the regards of review, the requirement is of existence of the consistent and sustainable frameworks that will include the AI literacy into the national and regional teacher development policies. The research can serve to influence the policy practices since the focus is made on equity, the moral righteousness governing and the institutional ability development of the AI introduction into the educational process and is necessary to ensure that the introduction of AI does not encourage inequalities but, on the contrary, advances the inclusion and sustainability of the systems.
8. Implications and Future Directions
The policy implications of the findings of the research conducted by this review are just alarming on the policy, professional development, and future research. The problems of AI literacy in teacher development should also be addressed simultaneously with multi-level responses that will act as perceiving AI literacy as a systemic and equity-based priority and not as a specific technology.
8.1. Recommendation at the Policy Level
Under policy measures, accountability reveals a lack of closeness between providing a coherent national and regional approach which directly feeds into the teacher development paradigms with AI literacy. It cannot be a plan, which can offer content to the policymakers as a partial, or pilot based project and then delineate explicit directions, which will be superimposed on the principles of curriculum, stipulations of professional growth, and ethical one-dimensional management of AI education. Such policies are supposed to target equal access to digital infrastructure, viable finances and construction of institutions, especially in the under-developed and the developing societies.
The ethics that the AI education strategies integrate in their policy frameworks should also encompass ethical issues, including fairness, transparency and privacy of data and inclusivity. Policymakers may ensure the utmost level of certainty in relation to AI technologies by offering regulation and institutionalization to teachers so that they can be critical and responsible in their reactions to AI technologies. It is worthy to mention that AI literacy policies must be locally adjustable in such a way that they become variable and adaptive without being denied the ability to have ethical and pedagogical standards in common.
8.2. Recommendations of the Professional Development
Within the education providers and training institutions context of teachers, the findings mean much about the locally based, scalable and equity based model of professional development. The single event workshop approaches that are used in older years cannot be considered sufficient in the context of encouraging AI literacy because the landscape of AI technologies is quickly evolving. Instead, it is to be turned into continuous and practice process of the understanding of the technical learning and application of the same into teachings and ethical considerations.
There are positive prospects of scalability of virtual and blended professional learning models particularly in financially constrained environments. These forms alongside peer collaboration, mentoring and reflective practice may lead to the realization of continued engagement with the AI literacy and to contextual constraints. Professional development program must also consider that the teachers are the co-leaders of the learning and what they know based on experience and agency should be important when introducing the AI tools to the diverse classroom realities. Effect of equity must remain central to the focus and in a manner, that, teachers have the opportunities to receive professional development irrespective of geographical, institutional, and socio-economic backgrounds.
8.3. Future Research Directions
The review also declares other possible research opportunities. To begin with, they must compare and contrast the contexts in order to understand the development of AI literacy in the various education systems which are developed, developing, and under-developed. According to such studies, it is possible to learn how such interactions between AI and teachers are determined by the structural conditions, policy settings and cultural factors.
Second, the longitudinal research is needed to learn the development of the AI literacy of the teachers during the period of time and the way the professional development process encourages the pedagogical practice, moral judgment, and academic achievement of the students. Research The long-term study would prove extremely useful in assessing sustainability and efficacy of various interventions of AI literacy.
Lastly, there are ethical and cultural concerns of AI implementation in education to which one should be more disposed. The next-generation study must take into account the way in which these local values and cultural norms and linguistic situations were interacted with the global AI technologies and how the interactions may be mediated in practice by the said teachers. These dimensions must be tackled to examine the development of AI literacy models that are effective and at the same time become culturally responsive and socially just.
9. Conclusion
The idea of AI literacy is also becoming a major issue of the teacher professional growth in the increasingly AI-driven world. This review has shown that whereas an AI has a tremendous potential in facilitating teaching and learning, the development of AI literacy among teachers is curtailed by structural, pedagogical, ethical, and policy challenges, particularly in the underdeveloped and developing settings.
In a generalized synthesis of issues and course of action through a plethora of strands of literature, the review marks out that there has been a dire need to recreate teacher professional development through a multidimensional conceptualization of AI literacy so as to embrace technical capability, critical awareness and moral reasoning. Under the circumstances of structures of inequality, failure to take AI literacy into account would cause marginalization of teachers and learners in the global education changes.
At the same time, the directions to which this review has led demonstrate that with the pragmatic focus of professional learning frameworks, the lifetable virtual online design, and the participatory policy design, it is possible to achieve decent and sustainable AI literacy development. Teachers will then be empowered to act not necessarily as a user of AI technology as a platform upon which the professional agency operates but rather as a connoisseur of AI-mediated education, a critic and even the moral overseer of AI-mediated education.
To sum up, AI literacy as a professional development opportunity among teachers is the means to attain increased equity in education, its sustainability, and innovation. The education systems can leverage the power of the AI and maintain the inclusiveness and social responsibility of AI in the AI age by prioritizing the technological change within the education system according to the pedagogical and ethical issues and placing the teachers at the center of AI integration processes.
Abbreviations

AI

Artificial Intelligence

TPD

Teacher Professional Development

OECD

Organization for Economic Co-operation and Development
Author Contributions
Mst. Anjona Khatun: Conceptualization, Writing – original draft
Md. Saddam Hossain: Supervision, Methodology, Writing – review & editing
Tashikuzzaman: Data curation, Resources, Validation
Sumaya Sumi: Investigation, Visualization
Conflicts of Interest
The authors declare no conflict of interests.
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    Khatun, M. A., Hossain, M. S., Tashikuzzaman, Sumi, S. (2026). AI Literacy for Teacher Development: Challenges and Pathways in Developing and Underdeveloped Contexts. Teacher Education and Curriculum Studies, 11(1), 31-41. https://doi.org/10.11648/j.tecs.20261101.14

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    Khatun, M. A.; Hossain, M. S.; Tashikuzzaman; Sumi, S. AI Literacy for Teacher Development: Challenges and Pathways in Developing and Underdeveloped Contexts. Teach. Educ. Curric. Stud. 2026, 11(1), 31-41. doi: 10.11648/j.tecs.20261101.14

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    Khatun MA, Hossain MS, Tashikuzzaman, Sumi S. AI Literacy for Teacher Development: Challenges and Pathways in Developing and Underdeveloped Contexts. Teach Educ Curric Stud. 2026;11(1):31-41. doi: 10.11648/j.tecs.20261101.14

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  • @article{10.11648/j.tecs.20261101.14,
  author = {Mst. Anjona Khatun and Md. Saddam Hossain and Tashikuzzaman and Sumaya Sumi},
  title = {AI Literacy for Teacher Development: Challenges and Pathways in Developing and Underdeveloped Contexts},
  journal = {Teacher Education and Curriculum Studies},
  volume = {11},
  number = {1},
  pages = {31-41},
  doi = {10.11648/j.tecs.20261101.14},
  url = {https://doi.org/10.11648/j.tecs.20261101.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.tecs.20261101.14},
  abstract = {Artificial Intelligence (AI) gradually alters educational practice, presenting new demands to the pedagogical, ethical, and professional skills of teachers. However, the overall research about AI literacy and its role in teacher development is predominantly focused on the developed context, thus limiting our understanding of the challenges facing such teachers in developing and underdeveloped societies. To fill this gap, this paper conducts a critical thematic review of empirical studies, conceptual literature, and policy-focused studies published since 2019. The review puts together key challenges, including infrastructural barriers, limited accessibility to continuous professional development, the lack of pedagogical preparation, ethical and equity challenges, and inadequate policies. In addition to this, it outlines new directions, such as context-specific professional development models, scalable virtual learning platforms, equity-focused frameworks, and human-AI collaborative strategies. The review redefines teacher development as a kind of professional agency operating on the concept of AI literacy as the convergence of technical knowledge, pedagogical acuity, and critical ethical awareness. The study also emphasizes the importance of strengthening institutional support, policy coordination, and collaborative professional learning environments to foster sustainable AI literacy development among teachers. The results offer practical lessons on enhancing equitable, sustainable, and context-based AI adoption in contemporary education systems globally.},
 year = {2026}
}

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  • TY  - JOUR
T1  - AI Literacy for Teacher Development: Challenges and Pathways in Developing and Underdeveloped Contexts
AU  - Mst. Anjona Khatun
AU  - Md. Saddam Hossain
AU  - Tashikuzzaman
AU  - Sumaya Sumi
Y1  - 2026/03/30
PY  - 2026
N1  - https://doi.org/10.11648/j.tecs.20261101.14
DO  - 10.11648/j.tecs.20261101.14
T2  - Teacher Education and Curriculum Studies
JF  - Teacher Education and Curriculum Studies
JO  - Teacher Education and Curriculum Studies
SP  - 31
EP  - 41
PB  - Science Publishing Group
SN  - 2575-4971
UR  - https://doi.org/10.11648/j.tecs.20261101.14
AB  - Artificial Intelligence (AI) gradually alters educational practice, presenting new demands to the pedagogical, ethical, and professional skills of teachers. However, the overall research about AI literacy and its role in teacher development is predominantly focused on the developed context, thus limiting our understanding of the challenges facing such teachers in developing and underdeveloped societies. To fill this gap, this paper conducts a critical thematic review of empirical studies, conceptual literature, and policy-focused studies published since 2019. The review puts together key challenges, including infrastructural barriers, limited accessibility to continuous professional development, the lack of pedagogical preparation, ethical and equity challenges, and inadequate policies. In addition to this, it outlines new directions, such as context-specific professional development models, scalable virtual learning platforms, equity-focused frameworks, and human-AI collaborative strategies. The review redefines teacher development as a kind of professional agency operating on the concept of AI literacy as the convergence of technical knowledge, pedagogical acuity, and critical ethical awareness. The study also emphasizes the importance of strengthening institutional support, policy coordination, and collaborative professional learning environments to foster sustainable AI literacy development among teachers. The results offer practical lessons on enhancing equitable, sustainable, and context-based AI adoption in contemporary education systems globally.
VL  - 11
IS  - 1
ER  -

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  • Abstract
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  • Document Sections

    1. 1. Introduction
    2. 2. Conceptual and Theoretical Foundations
    3. 3. Methodology
    4. 4. Findings: Challenges for AI Literacy in Teacher Development
    5. 5. Findings: Pathways and Opportunities
    6. 6. Discussion
    7. 7. Contribution and Significance
    8. 8. Implications and Future Directions
    9. 9. Conclusion
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  • Abbreviations
  • Author Contributions
  • Conflicts of Interest
  • References
  • Cite This Article
  • Author Information

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