Integrating Sustainable Development Goals (SDGs) and Generative AI to Enhance Language Digital Literacy and Creativity in EFL Learning Environments

Summary

Combining SDGs with generative AI like ChatGPT boosts English learners' motivation, digital literacy, and creativity. Higher engagement strengthens this link, providing teachers with practical strategies for integrating AI in language teaching.

Abstract

This study investigates the integration of Sustainable Development Goals (SDGs) and generative AI, specifically ChatGPT, to enhance language digital literacy, creativity, and motivation in EFL learning environments. Adopting a quantitative, cross-sectional design, the data for this study were collected from n = 420 undergraduate EFL students at one public sector university in China, using validated scales to measure SDG integration (SDG), Use Generative AI like ChatGPT (UCGPT), digital literacy (DL) EFL Students' creativity (SC), EFL Students' motivation (SM) and language learning engagement (LLE). Partial least squares structural equation modeling (PLS-SEM) was employed to analyze direct, mediating, and moderating effects. Findings revealed that SDG integration and UCGPT were significantly associated with SM, which in turn predicted digital literacy and EFL students' creativity. Moreover, LLE moderated the relationship between DL and SC, while SM mediated the effects of SDG integration and UCGPT on digital literacy. These results highlight the transformative potential of integrating SDGs and AI tools in EFL education, providing scalable strategies to develop 21st-century skills. Additionally, the study provides concrete strategies for EFL teachers, such as tasking students with using ChatGPT to prepare for debates on SDG topics, like climate justice, or to co-create multilingual social media campaigns for sustainability awareness. These applications demonstrate how to operationalize SDG-AI integration in everyday lesson planning.

Introduction

The global landscape of English as a Foreign Language (EFL) education is undergoing a profound transformation, driven by the dual imperatives of technological innovation and the United Nations' Sustainable Development Goals (SDGs)¹. While the demand for English proficiency continues to surge in non-Anglophone regions, fueled by globalization, academic mobility, and digital economies², traditional pedagogical approaches often fail to equip learners with the critical competencies needed for 21st-century communication³. Conventional EFL methodologies, which emphasize rote memorization and structural accuracy over creative expression and digital engagement, risk exacerbating disparities in access to quality education⁴. This misalignment not only hinders progress toward SDG 4 (inclusive and equitable quality education) but also overlooks the transformative potential of emerging technologies⁵, particularly generative artificial intelligence (AI), in reshaping language learning ecosystems⁶. Despite the rapid expansion of AI-driven tools like ChatGPT, their integration within EFL contexts remains underexplored, particularly in terms of sustainability frameworks⁷. This study bridges this critical gap by investigating how the strategic convergence of SDGs and generative AI can enhance digital literacy, creativity, and motivation in EFL learning environments, advancing pedagogical innovation and global sustainability agendas. Significant gaps persist in the existing literature regarding the intersection of AI-assisted language learning and SDG-aligned education⁸. While prior research has examined AI's role in personalized language instruction⁹, automated assessment¹⁰, and virtual exchange programs¹¹, these studies rarely engage with sustainability frameworks. Conversely, literature on EFL and sustainability has predominantly focused on content-based instruction (environmental texts) rather than leveraging technology to restructure pedagogical paradigms¹². This disconnect is particularly problematic given the escalating digital divides in Global South contexts, where linguistic marginalization intersects with limited access to cutting-edge educational tools¹³. Furthermore, while SDG 4.4 explicitly prioritizes digital literacy, most EFL curricula fail to link digital competence with sustainability outcomes, leaving learners ill-prepared for the demands of employment, entrepreneurship, and decent work¹⁴. The absence of an integrative framework that aligns AI-driven pedagogy with sustainability competencies represents a critical oversight¹⁵. Moreover, the framework is readily applicable in undergraduate EFL courses where students possess basic digital literacy for operating generative AI tools like ChatGPT. Implementation is designed for standard technology-integrated classrooms, requiring educators to provide structured, SDG-aligned task prompts to guide the AI collaboration effectively.

This study fills these gaps by proposing a novel, SDG-informed generative AI framework for EFL education, grounded in sociocultural theory¹⁶ and critical digital literacies¹⁷. Unlike prior work that treats AI as an instructional tool, researchers position it as a collaborative partner in scaffolding learners' creative and critical thinking¹⁸. This shift aligns with SDG targets on inclusive, learner-centered education. Empirically, researchers employ partial least squares structural equation modeling (PLS-SEM) to analyze data from n = 420 Chinese undergraduate EFL learners, examining how SDG integration (SDG) and ChatGPT use (UCGPT) jointly influence motivation (SM), digital literacy (DL), Students 'creativity (SC), and language learning engagement (LLE). The theoretical contributions of this study are threefold. First, researchers extend self-determination theory (SDT) by demonstrating how SDG-aligned AI tools satisfy learners' intrinsic needs for autonomy, competence, and relatedness, enhancing motivation. Second, researchers refine engagement theory by revealing how digital literacy and creativity interact dynamically in AI-mediated environments, contingent on learners' engagement levels. Third, researchers advance critical digital literacies by embedding ethical AI use within sustainability discourse, urging learners to interrogate data biases, environmental costs, and labor practices in AI systems. Empirically, the findings provide actionable insights for educators and policymakers, illustrating how ChatGPT can democratize access to SDG-related language tasks (climate change debates, gender equality simulations) while fostering higher-order cognitive skills. This article is structured as follows: First, the introduction that discusses the significance/originality of this research, the existing gap in the literature, and the contribution of this study. Next, there are reviews of the literature on SDGs in EFL, generative AI, and digital literacies. The Protocol section details the PLS-SEM methodology, while the Results section presents the findings. The Discussion section elaborates on the key findings and theoretical and practical implications. Finally, the article concludes with limitations and future directions.

Literature review and hypotheses development

Sustainable Development Goal 4 and EFL Students' Motivation
A pedagogical approach that integrates English as a Foreign Language (EFL) curricula with Sustainable Development Goal 4¹⁹, which emphasizes inclusive and equitable quality education and lifelong learning for all, has emerged as a key strategy for enhancing student motivation²⁰. SDG 4's targets, specifically 4.4 (skills for employment) and 4.7 (education for sustainable development), underscore the importance of developing linguistically competent learners with critical thinking, digital literacy, and ethical engagement skills²¹. The principles of these competencies are additionally supported by the Responsibility to Protect and the 'Rights-Based Education' SDG 4.5 targets. Several studies suggest that when climate action, gender equality, and fighting poverty are covered in language learning, it promotes students' motivation for the subject²². This method supports the Self-Determination Theory^23,24, which believes that the experience of autonomy, mastering skills, and social connection keeps people motivated²⁵. Generative AI works even better as a motivation tool when interactive content related to Sustainable Development Goals is used²⁶. AI with chatbot simulated summits and platforms in several languages that focus on sustainability make it possible for students to learn about complex subjects through language exercises²⁷. Applying scenarios that require students to discuss water scarcity solutions in English helps them become interested. It sharpens their problem-solving abilities and gives them a greater sense of being in charge and importance²⁸. AI feedback is standardized and given instantly (which was pointed out, helping meet the diverse educational needs, and it supports equity as explained in SDG 4²⁹, all of which calms students and ensures equality. Educational experts need to take SDG connections a step further since they should focus on developing skills that enable students to identify systemic inequalities³⁰. When there is an AI gap between developed and underdeveloped countries, this increases the number of reasons for students to feel discouraged and calls for inclusive design to be adopted³¹. Based on the above literature, this hypothesis is proposed: H1: Integrating SDGs into EFL lessons increases students' motivation to learn English.

Generative AI Usage and EFL Students' Motivation
Generative AI tools, which include language models and chatbots, are increasingly valued for helping to boost motivation in EFL education by encouraging interaction, addressing personal needs, and promoting creative activities³². These approaches enable learners to interact directly with AI, practice communicating in realistic situations, and receive quick and adjustable feedback, which is known to lower anxiety and sustain their interest³³. For instance, AI-generated educational resources, such as personalized texts and scenarios tailored to a student's level and interests, can meet their psychological needs for autonomy and a sense of capability. Since generative AI can provide resources in multiple languages and consider cultural contexts, it enables learners to explore global issues in English, prompting them to think deeply about ethical decisions³⁴. However, experts advise that relying too heavily on AI may limit communication among people and prevent them from developing critical thinking, both of which are essential for children's growth. In addition, disadvantages in internet access and the effects of biased algorithms can exacerbate existing inequalities for certain learning groups, further deepening current disparities. Based on the above literature, the following hypothesis is proposed: H2: Using ChatGPT in EFL classrooms increases students' motivation to engage in language learning.

EFL Students' Motivation and Digital Literacy
The relationship between digital literacy and EFL motivation is not merely correlational but synergistic. Students with higher digital literacy are better equipped to seek out and engage with authentic, multimodal content that aligns with their personal interests and sociocultural contexts^35,36. This capacity directly fuels intrinsic motivation by enhancing learners' sense of autonomy and competence, as they can navigate digital environments more effectively. However, this positive cycle is not automatic. The motivational impact is contingent on the quality of digital engagement; simply using technology does not guarantee motivation unless the tasks are meaningful and culturally responsive³⁷.

Moreover, although Generative AI is currently being marketed as a mechanism to accelerate this synergy with the help of personalized scaffolding^38, a critical approach reveals that there are several contingencies to this. The success of AI technologies in facilitating cross-cultural dialogue and demystifying media bias³⁹ is based on equal access and underlying digital competency, which is frequently lacking in under-resourced environments⁴⁰. This results in a motivational gap, with digital illiteracy causing frustration and a sense of disengagement, thereby sabotaging the very motivation AI aims to generate⁴¹. Thus, AI integration should be accompanied by rigorous digital literacy models that encourage the learner to understand ethical connotations, so that inadvertent propagation of current imbalances does not occur with the use of technologies⁴². Based on the above literature, the following hypothesis is proposed: H3: Higher motivation in EFL students leads to improved digital literacy skills.

EFL Students' Digital Literacy and EFL Students' Creativity
In English as a Foreign Language (EFL), digital literacy catalyzes creativity by empowering learners with the technical and critical ability to re-contextualize language use by innovative digital modes⁴³. Students become proficient at using devices such as generative AI, multimedia platforms, and collaborative virtual spaces, allowing them to move beyond text-based exercises and use them as tools for creating multimodal projects like digital stories, podcasts, or even AI-produced narratives⁴⁴. For example, AI-based platforms for co-creating multilingual poetry or simulating virtual reality scenarios help learners to experiment with linguistic and cultural hybridity and combine language acquisition and artistic exploration⁴⁵.

Digital literacy correlates with creativity through equal access opportunities and teaching approaches with defined purposes⁴⁶. Some marginalized groups lack equal access to generative AI and digital tools, resulting in creative stifling and passive learning rather than active creation⁴⁷. Educational professionals should select critical methods that focus on sustainable teaching practices to help their students examine both the ethical approach to AI content generation and the digital workflow impacts on the environment⁴⁸. Students can develop ethical craftsmanship through hands-on digital design activities that combine sustainable project development with algorithmic bias examination, strengthening SDG 4.7's sustainability and justice targets. Based on the above literature, the following hypotheses are proposed: H4: EFL students with stronger digital literacy skills demonstrate greater creativity in language tasks. H5: EFL students' language learning engagement demonstrates greater creativity in language tasks.

Moderating Effect of Language Learning Engagement
Specifically regarding EFL learning contexts, pedagogical interventions that integrate Sustainable Development Goals (SDGs) and generative AI are more effective when language learning engagement, as defined by the cognitive, behavioral, and emotional investment in language acquisition, is a multilaterally pivotal moderating variable⁴⁹. In implementing SDG-related content, high levels of engagement increase the motivational and cognitive gains, as participants in SDG-aligned project-based tasks or AI-simulated tasks are more persistent, ask more questions, and claim more ownership over their learning journeys⁵⁰. For example, student co-creation of artificial intelligence (AI) sustainability campaigns or discussion of global issues in English are shown to enhance linguistic capabilities and creativity, given the students' emotional connection to real-life problems⁵¹. Engagement is a moderator determining how learners handle ethical and cultural aspects of EFL learning environments driven by AI. Students who demonstrate disengagement view AI as an authoritarian system that intensifies their concerns and leads them to passively depend on automated feedback⁵². Based on the above literature, the following hypothesis is proposed: H6: Language learning engagement moderates the relationship between digital literacy and creativity.

Mediating Effect of EFL Students' Motivation in EFL Settings
The motivational levels of EFL students play a vital role in how pedagogical strategies linking to SDGs and using generative AI affect their digital skills and creative abilities as well as their proficiency in the English language⁵³. Instructional design meets learner achievement through motivation, which uses Self-Determination Theory⁵⁴, to strengthen psychological conditions for deep engagement. Student motivation related to autonomy (decision-making for advocacy topics), competence (learning sustainability vocabulary), and relatedness (participating in global activities) serves as the mechanism through which SDG-themed content determines the effectiveness of students' critical thinking and digital skill development. Student engagement can rise during the first use of generative AI tools because of their newness, but ongoing improvements in literacy and creativity need students to recognize AI interactions as both identity-relevant and autonomy-promoting⁵⁵.

Researchers establish motivation as the primary factor that connects various settings through empirical evidence. SDG-focused projects exposed to EFL learners led to associated creativity because the learners experienced heightened motivation levels simultaneously, as reported by self-assessments⁵⁶. Writing outcomes from AI-generated personalized feedback showed maximum improvements in students who demonstrated strong existing motivation^57,58. The study proved that motivation regulates the effectiveness of technological interventions. Another factor that hinders this process is cultural, along with socioeconomic elements. Resource-challenged settings tend to prioritize external incentives, which relate to career opportunities mentioned in SDG 4.4, resulting in altered relationships between motivation and digital tool delivery^59,60. Based on the above literature, the following hypotheses are proposed: H7: Students' motivation mediates the relationship between ChatGPT use and digital literacy skills. H8: Students' motivation mediates the relationship between SDGs and digital literacy skills. Figure 1 shows the research model of the present study, which includes all variables and their path relationships with each other. Moreover, based on the path relationships between variables, all hypotheses are also indicated in the research model.

Protocol

This study was conducted in compliance with ethical guidelines for human subject's research. Informed consent was obtained from all participants prior to their involvement, ensuring they were fully informed of the study's purpose, survey-based procedures, voluntary nature, and their right to withdraw at any time without consequence. All methods adhered to ethical standards for human research, ensuring participant confidentiality, data anonymization, and protection throughout the study. The study involved non-invasive, survey-based data collection with minimal risk to participants.

1. Research design

This study adopted a quantitative, cross-sectional research design to examine the relationships between Sustainable Development Goal (SDG) integration, generative AI usage, digital literacy, creativity, motivation, and language learning engagement in EFL contexts. The design prioritized hypothesis testing through structured surveys, grounded in positivist epistemology, enabling statistical generalization of findings across similar academic settings. A partial least squares structural equation modeling (PLS-SEM) approach was selected to analyze complex mediation and moderation effects. PLS-SEM accommodates smaller sample sizes and exploratory model structures while minimizing assumptions about data normality⁶¹. The design aligned with SDG 4's call for data-driven educational strategies, focusing on measurable outcomes to inform scalable pedagogical innovations in AI-enhanced language learning.

2. Data and sampling

First, to construct the sampling frame, researchers identified and selected all intact, technology-integrated EFL classes from the university's program roster for the Spring 2024 semester, resulting in a convenience sample of 420 undergraduate students. Second, participant eligibility was confirmed against the criteria of being aged 18-24 and enrolled as first- or second-year undergraduates with an intermediate level of English proficiency, as per university placement records; the final cohort comprised 62% female and 38% male participants, with 95% originating from Eastern China. Third, during a scheduled class session, the researcher administered the survey instrument in person: after distributing a detailed information sheet, the researcher read aloud the informed consent script, allowed time for questions, and collected signed consent forms before distributing the questionnaire, which participants had 20 min to complete anonymously with no incentives provided. Fourth, to control for the extraneous variable of prior AI experience, the survey instrument itself incorporated a dedicated filter section at the outset, capturing data on participants' familiarity with AI tools for subsequent statistical control. Finally, the collected data were screened for completeness, and the sample size of n = 420 was verified for statistical adequacy by applying the "10 times rule" for PLS-SEM, confirming it exceeded the minimum requirement of 10 cases per predictor variable in the model's most complex regression equation.

1. Practical integration of SDGs and ChatGPT
   To ground the survey measures in a concrete pedagogical context, the study was conducted within a specific EFL module that explicitly integrated SDG themes with ChatGPT assistance. Students participated in a series of task-based learning activities over a four-week period. For instance, one task required students to use ChatGPT to research a specific SDG (SDG 4 education, SDG 13: Climate Action, or SDG 5: Gender Equality), generating a summary of key issues and then critically evaluating the AI-provided information for potential bias. In another assignment, students used ChatGPT as a collaborative tool to co-write a persuasive essay or a social media campaign script advocating for a local sustainability initiative, thereby connecting global goals to their immediate context. These tasks were designed to be multimodal, often culminating in video blogs or presentations, requiring students to synthesize information, exercise critical thinking, and use English for a tangible purpose. This practical implementation ensured that students' survey responses were informed by direct experience with the integrated SDG-AI learning approach.

3. Ethical considerations

The present study was conducted in accordance with rigorous ethical considerations. All participants were informed about the purpose and procedures of the study, as well as their rights, in detail through both written and oral means, and then provided informed consent before participating in the study. This agreement ensured anonymity, allowing them to withdraw at any time without reprisals and guaranteeing the confidentiality of their data. The information was gathered through encrypted digital systems and stored securely in password-protected servers. All analysis was conducted on aggregated data to prevent the identification of any individual. Consistent with the SDG 4.7 principles, the study included a separate debriefing section to address ethical issues unique to generative AI, clearly presenting potential concerns such as the possibility of biases in ChatGPT responses and its environmental impact. Lastly, the study's limitations, such as self-reporting biases and the geographical location of the sample, were openly acknowledged to promote academic integrity.

4. Measures

Constructs were operationalized using validated scales adapted to the EFL context. SDG integration was measured using a 36-item scale with three subscales: economy (13 items), society (9 items), and environment (14 items). This scale was derived from Atmaca et al.⁶², assessing exposure to sustainability themes in language tasks. Digital literacy was evaluated using a 29-item scale, expanded to six dimensions adapted from Rodríguez-de-Dios et al.⁶³, including Technological Literacy (7 items), Personal Security Literacy (5 items), Critical Literacy (5 items), Device Security Literacy (4 items), Information Literacy (5 items), and Communication Literacy (3 items). Moreover, EFL Students' Creativity was captured through Govindasamy et al.⁶⁴, a 12-item instrument, covering four subscales, each with three items: originality, flexibility, fluency, and elaboration. ChatGPT usage was quantified via an 8-item scale adapted from Abbas et al.⁶⁵, focusing on frequency, purpose, and perceived efficacy in language tasks. Motivation was assessed using Obiosa⁶⁶, with a 05-item scale. Finally, language learning engagement was evaluated using a 9-item scale from Eerdemutu et al.⁶⁷. All scales employed 5-point Likert responses, with pilot testing (n=30) confirming reliability (Cronbach's α > 0.82) and confirmatory factor analysis validating construct distinctiveness. The analysis for the present study was conducted based on first-order reflective-reflective constructs, as second-order reflective-reflective constructs are not directly tested.

5. Data analysis

The data analysis was executed through a sequential two-phase procedure utilizing SPSS version 28 and SmartPLS version 4. The process commenced in SPSS 28, where researchers navigated to Analyze > Descriptive Statistics > Descriptives and Analyze > Correlate > Bivariate to compute preliminary descriptive statistics (means, standard deviations) and Pearson correlation coefficients, respectively, for the sample of n = 420 cases. Subsequently, the primary analysis shifted to SmartPLS 4 to conduct Partial Least Squares Structural Equation Modeling (PLS-SEM). The first step in SmartPLS involved validating the measurement model by running the built-in Calculate algorithm to assess internal consistency, requiring composite reliability values to exceed the threshold of 0.70, and convergent validity, confirmed by an Average Variance Extracted (AVE) greater than 0.50 for all constructs. Discriminant validity was then verified using the software's report functions to apply the Fornell-Larcker criterion and ensure all heterotrait-monotrait (HTMT) ratios were below the conservative benchmark of 0.85. Following this validation, the structural model was evaluated by using the Bootstrapping routine configured with 5,000 subsamples and a two-tailed test at the 0.05 significance level to generate bias-corrected confidence intervals for all path coefficients (β) and indirect effects, thereby testing the hypothesized direct, mediating, and moderating relationships. Finally, the model's predictive relevance was ascertained by examining Stone-Geisser's Q² value, obtained through the Blindfolding procedure, with values above zero indicating adequate predictive power, and the substantive impact of predictors was determined by calculating effect sizes (f²) from the PLS algorithm results.

Results

Descriptive statistics
A descriptive analysis provides important insights into patterns of variables, central locations, and their dispersion, enabling researchers to verify the health, normality, and theoretical congruence of the initial data. In this study, the mean values are used to measure the level of kurtosis, while the parameters of standard deviation are employed to assess the level of skewness. Additionally, the effects of skewness are also considered. This suggests that the 420 participant...

Discussion

Summary of key findings
This study examined the interplay between Sustainable Development Goals (SDGs), generative AI (ChatGPT), and key educational outcomes: motivation, digital literacy, and creativity in EFL learning environments. The results confirmed all direct-effect hypotheses (H1-H5), demonstrating that SDG integration (β = 0.37) and ChatGPT use (β = 0.28) significantly enhance student motivation, which in turn fosters digital literacy (β = 0.45) and creativity (β = 0.32...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
ChatGPT Usage Scale	8-item scale adapted from Abbas et al. (2023).	Abbas et al. (2023).
Creativity Scale	12-item scale adapted from Govindasamy et al. (2022), measuring four sub-constructs: Originality, Flexibility, Fluency, and Elaboration.	Govindasamy et al. (2022)
Data Collection Instrument	Anonymous, self-administered paper-and-pencil questionnaire.	Not applicable / custom-designed
Data Storage System	Password-protected servers with encrypted digital systems.	Not applicable / institutional IT infrastructure
Digital Literacy Scale	29-item scale adapted from Rodríguez-de-Dios et al. (2018), covering six dimensions: Technological, Personal Security, Critical, Device Security, Information, and Communication Literacy.	Rodríguez-de-Dios et al. (2018)
Informed Consent Documents	Written information sheet and consent form.	Not applicable / custom-designed
Language Learning Engagement Scale	9-item scale adapted from Eerdemutu et al. (2023).	Eerdemutu et al. (2023)
Motivation Scale	5-item scale adapted from Obiosa (2023).	Obiosa (2023).
PLS-SEM Algorithm & Bootstrapping	SmartPLS 4 internal routines (PLS Algorithm and Bootstrapping with 5,000 subsamples).	https://www.smartpls.com/documentation/algorithms-and-techniques
Predictive Relevance Assessment	Blindfolding procedure in SmartPLS 4 to calculate Stone-Geisser’s Q² value.	https://www.smartpls.com/documentation/predictive-relevance-q2
SDG Integration Scale	36-item scale adapted from Atmaca et al. (2023), with subscales for Economy (13 items), Society (9 items), and Environment (14 items).	Atmaca et al. (2023)
Statistical Analysis Software	IBM SPSS Statistics, Version 28.	-
Structural Equation Modeling Software	SmartPLS, Version 4.	https://www.smartpls.com
Validity & Reliability Checks	SmartPLS 4 reporting functions for Composite Reliability, Average Variance Extracted (AVE), Fornell-Larcker Criterion, and HTMT ratios.	https://www.smartpls.com/documentation/reporting