https://doi.org/10.53656/ped2026-4s.02

Резюме. The linguistic output evolvement of artificial intelligence (AI) tools and its implications for the pedagogical practice in language education merit detailed attention. There has been research on AI-generated texts in adult education but the potential of such texts for engaging young learners’ interest needs further exploration. The present study aims at responding to this existing research gap. A survey questionnaire and two narratives were used in order to test the extent to which 210 trainee language teachers perceive AI-generated and human-authored stories as engaging for young learners and, thus, promoting situational interest. It also tested the relations between participants’ language proficiency levels, skills to use AI tools and teaching competence. Findings revealed that future teachers have acquired in varying degrees the ability to identify the potential of narratives to engage young language learners. Their skills in using AI tools were correlated to their preferences for a particular type of text.

Ключови думи: AI-generated stories; early language teaching; situational interest; text engagement

Introduction

Early foreign language teaching practices incorporating readalouds and storytelling benefit language and overall personal development in many ways. When selecting tales with a view to achieving specific pedagogical goals, language teachers need to bear in mind the importance of promoting young learners’ sense of achievement and confidence in comprehending the story. The enjoyability and overall appeal of narrative material, selected by teachers, enhance language progress (Tragant & Vallbona, 2018). Teaching languages through stories represents a holistic pedagogical practice as it provides cognitive, affective and social engagement (Cameron & Mckay, 2010; Vecino, 2006). Fiction narratives help enhance learners’ language acquisition by providing a meaningful context for language use (Ghafar, 2024). Stories ensure exposure to authentic linguistic material (Cole et al., 2017), thus providing a direct and enjoyable immersion in the new language (Vardell et al., 2006). From a psychological perspective, engagement is viewed as conducive to higher levels of flourishing and satisfaction (Bakracheva, 2025). In the field of language teaching, learners’ engagement and sense of personal fulfilment contribute to sustainable language progress. We view positive language pedagogies through the prism of designing learning experiences that foster the sense of delightful exploration.

The present study adopts such a constructive, learner-centered pedagogical approach to language teaching. It explores how undergraduate and postgraduate students of English language engage with human and artificial intelligence (AI)authored stories for children, on the one hand, and how in their capacity of trainee language teachers they perceive these texts as conductive to the triggering of situational interest in young children. To the best of our knowledge, stories aimed at young language learners have not been the object of research on comparing the potential of human or AI creative output in the narrative genres.

1. Key theoretical aspects

1.1. From narrative engagement to situational interest

When interacting with a story – be it in written or oral form – language learners create mental images, sparked by the narrative elements. If the narrative content, generated by linguistic means, fails to trigger the ability to visualise, then the entire experience will lack in pedagogical and psycho-emotive value. The cognitive and emotional involvement with a story and its characters results in a mental state which favours the activation of “attention, imagery, and emotion” (Appel et. al., 2021, p. 1). Being immersed or fully transported into a narrative is constituted as the state of narrative engagement or, alternately, narrative transportation (Bilandzic et al., 2019; Green & Appel, 2024). Our observations indicate that when the young learners’ imagination is fascinated by the plot of a tale, they are cognitively and emotionally more involved in the learning process.

The more intensely the learner engages with the text, the better their overall comprehension of it would be (Green & Dill, 2013). Thus, the selection of stories to be used in language instruction needs to take into account how comprehension is impacted by factors such as “content accessibility” (Vardell et al., 2006, p. 735). The vivid visualisation experienced in the course of narrative transportation (Green, 2004) is consistent with the findings of a study, which established significantly higher rates of fiction text comprehension compared to nonfiction text comprehension among schoolchildren (Chung et al., 2023). Among the factors that contribute to the appeal of narratives, used for instructional purposes, are their consistency (Alhussain & Azmi, 2021) and vividness (Schraw et al., 2001). Therefore, the careful attention to story content and form is an important factor when language teachers select teaching materials.

Additionally, nonverbal cues are of particular relevance when a story is shared orally, especially given the appropriateness of readalouds and story-telling in early language teaching of pre-literate learners. The role of nonverbal cues on engagement with a narrative is convincingly highlighted by the findings of a survey among young adults, listening to a robot telling a tale, wherein listeners are “more deeply transported into the story when it is told by a robot that shows story-congruent emotional expressions” (Appel et al., 2021, p. 7). The role of other paralinguistic features – such as the effect of human versus software-generated voice – is exemplified in research which established the greater enjoyability of human-narrated stories for the listeners (Rodero & Lucas, 2023). In the light of inclusive pedagogical practices, it would be fair to mention the positive results in vocabulary acquisition for English language students with hearing loss, obtained through interactions with a computer-based tool enhancing lip reading (Zamfirov & Saeva, 2013).

Authentic fiction materials can be efficiently used in language instruction when appropriately matched to the particular learners’ needs and interests. The construct of interest is generally viewed as comprising components related to both cognition and affect (Hidi & Renninger, 2006; Rotgans & Schmidt, 2011). Individual interest is defined as “a relatively enduring predisposition to attend to objects, events, ideas, etc., and to reengage with particular content” (Hidi, 2006, p. 72). Thus, individual interests are aspects of the learner’s personality that lie outside the scope of teacherprovided motivational input and approach.

Situational interest, on the other hand, is “environmentally triggered” (Hidi, 2006, p. 72) or activated by “text features” (Hidi & Renninger, 2006, p. 114). Thus, what efficient language teaching pedagogies strive to achieve is sparking and maintaining the situational interest in learners. A study in this field suggests that “when filled with a higher interest level in an L2 class, [students] come up with successful learning as a result of being more attentive and respectful to some immediate elements in the teaching context, such as the teacher, materials, and classmates” (Asgari et al., 2019, p. 70). Additionally, viewed from a speech development perspective, situational interest enhances the development of children’s language skills (Dunst et al., 2016).

Educational materials also can play a positive role in increasing situational interest in language learning settings. Vividness of texts (Schiefele, 2009) and the underlying element of surprise (Hidi & Renninger, 2006), along with coherence and relevance (Schraw et al., 2001), are factors that render a text beneficial for triggering situational interest. Particularly relevant to the purposes of the present research is the understanding that “text-based interest and situational interest are often used synonymously” (Schiefele, 2009, p. 199). The capacity of stories to engage learners by ensuring meaningful interactions with the target language, combined with the learning benefits brought about by the fact that “cognitive and affective systems are involved to varying degrees in the processing of interesting texts” (Hidi, 2006, p. 72) provide a sustainable rationale for their use in language learning.

1.2. AI capacity for narrative output

Given the premise that pre-trained large language models (LLM) have “high abilities in processing natural languages” (Alhussain & Azmi, 2021, p. 16), it is now evidenced that the freely accessible version of ChatGPT can generate fluent texts (Kohnke et al., 2023) which resemble human-created written output (Zhu et al., 2023) and imitate human reasoning (Shah, 2023). Furthermore, the ability of this chatbot to structure information on creating educational content is also verified (Totseva & Mavrodieva, 2024).

A specific feature that ChatGPT shares with other LLMs is the functionality to make viable predictions for the next word in any given sequence (Ouyang et al., 2022). This particular AI model is “trained to predict the right combination of words that a human might produce” (Shah, 2023, p. 23). Being pre-trained on massive textual data (Radford et al., 2018; Roumeliotis & Tselikas, 2023), ChatGPT has been continuously evolving and successfully simulates human-like fluency in its written and oral output.

When AI-powered pre-trained models generate narrative texts, they again rely on predictions, based on narrative elements incorporated into the large data structures they learn from (Alhussain & Azmi, 2021). LLMs are capable of predicting types of narrative structure (Chu & Liu, 2024), as well as generating consistent texts in various genres (Köbis & Mossink, 2020).

Previous research shows that humans find it hard to distinguish between AIgenerated or human-authored texts (Shah, 2023). Particularly pertinent from a linguistic perspective we consider the finding that the AI output is so fluent that linguistic factors such as grammatical, punctuational or stylistic correctness provide insufficient clues when it comes to differentiating between AI and human-authored narratives (Clark et al., 2021). Yet, other research results indicate that natural human language, compared to AI-generated output, is much more persuasive in expressing nuances of emotions (Binder, 2022). Furthermore, there are indications that prior information about the AI real or alleged authorship of a story leads to lesser engagement (Chu & Liu, 2024) compared to responses to human-written stories.

1.3. Language teachers’ perceptions of AI usability in foreign language teaching

The evolvement of AI tools accounts for the increased interest in exploring the perceptions of teaching professionals of AI-powered platforms, tools or output in educational practices. Teachers’ viewpoints on implementing AI in language classrooms seem so far rather heterogenous.

A recent review of research on the usability of ChatGPT in language teaching in higher education sheds light on use of this generative AI model as an adaptable “auxiliary tool” (Gutai, Klímova & Lora, 2024, p. 8) for the development of various linguistic skills. Additionally, a metaanalysis on applying AI in pronunciation instruction shows that the teaching community has made so far limited use of AI tools for teaching this specific linguistic aspect (Vančová, 2023) compared to teaching vocabulary and grammar.

Particularly promising as to language instructors’ increased awareness of benefits offered by AI technologies is the observation that incorporating AI tools may result in “enhancing students’ motivation and student-centered learning” (Abdelatif & Siddiqui, 2021, p. 211). This finding is in line with the generally positive views of respondents in another survey on the impact AI technologies have on language teaching practices, as AI is deemed to help language instructors “make their assessment procedures more objective” (Pokrivcakova, 2023, p. 107).

Last but not least, research in AI incorporation in language teaching increasingly calls for the provision of novice teachers with supplementary training on using AI tools (Galán-Rodríguez et al., 2025). This need for additional support is convincingly highlighted by the results of a recent survey on how primary school teachers across schools in Bulgaria use digital technologies in their teaching practice (Aleksieva, Racheva & Peytcheva-Forsyth, 2025). The study registers isolated instances when AI tools such as Duolingo and the game-based tool Kahoot, which incorporates AI features for tailored content creation, are utilized in primary school language instruction to enhance motivation and engagement. The above findings correspond closely to the established needs of preschool teachers for ongoing training that would equip them with the abilities to use educational technological tools (Veličković & Stošić, 2016).

Taking everything mentioned above into consideration, the present study seeks to contribute to emergent research in AI applicability in early foreign language instruction. As far as we can ascertain, no other theoretical or practical exploration has addressed usability of AI-authored narrative output in teaching languages to children. Thus, our aim was to test how trainee language teachers view the engagement with AI-written stories from their own perspective as selectors of language teaching materials and from the perceived perspective of young learners themselves.

2. Research design and scope

2.1. Research questions and objectives

The study described in this article is driven by the increasing usability of AI in foreign language teaching and learning. It aims at addressing the following specific research questions:

Q 1: Are there meaningful relations between trainee language teachers’ English proficiency, their AI usage skills and teaching competence?

Q 2: How well can trainee language teachers’ identify human and AI-generated texts?

Q 3: What are the trainee language teachers’ preferences for the two types of narrative texts?

Q 4: What are the trainee language teachers’ presumptions of children’s preferences for the two types of stories?

Q 5: Which demographic characteristics are related to trainee language teachers’ text choices?

2.2. Research method

The present research was conducted at two universities in Bulgaria in 2025. The study sample was a non-random sample of convenience and these two higher education institutions were selected because they offer a wide range of English language studies programmes, attracting undergraduate and postgraduate students from all over the country and abroad. It intended to explore trainee language teachers’ engagement with short narrative texts generated by humans or AI, their perceptions of how these narratives would activate situational interest in young learners of a foreign language, and test if participants’ English language proficiency, their skills of using AI tools and their teaching competence would be related to textual preferences. To address the five research questions (Q1 – Q5), defined above, a survey questionnaire and two narratives were used. This study was part of a larger investigation into trainee language teachers’ professional preparedness and their knowledge of and preferences for different AI-produced and human-authored fiction forms.

The survey questionnaire was uploaded on the Moodle e-platform of the two universities. Students who were being trained to become teachers of English in diverse degree programmes were invited to take part on a voluntary basis. All ethical norms and code of research conduct were strictly followed.

First, general information on the demographic characteristics of the respondents was collected. Subsequently, the trainee teachers were asked to self-assess their English language proficiency, teaching competence and using AI technologies on a 5-point Likert scale (ranging from minimal – 1, to excellent – 5). The third part of the survey instrument contained two narrative stories which the participants were invited to read and comment on. The questions in this section gathered information on respondents’ own preferences for different narratives and their views on how young language learners would respond to the same narratives. Finally, the students were invited to self-report on a 4-point Likert scale (from “not at all” confident – 1, to “very” confident – 4) on their ability to recognize which narrative was AIgenerated and which was human-written.

The two stories used in our study were carefully selected. The human-authored story for young learners was chosen from the rich collection of stories, offered as learning resources on the LearnEnglish Kids section of the British Council’s website https://learnenglishkids.britishcouncil.org. The selected story was ‘The treasure map’1. Our choice of this particular narrative for young learners was based on the perceived engageability of adventure tales, revolving around treasure hunting; the simplicity and clarity of language use, as well as the brevity of the text.

The AI narrative, used in this research (see Appendix), was generated over the course of a 10-minute written exchange with ChatGPT-3.5 in November 2024. Particular prompts, related to the plot and character elements of the original, human-written story, had been devised in advance. Since the initial output from the LLM was found to correspond with the research purpose, no additional exchange to fine-tune the AI narrative was deemed necessary.

2.3. Results

Altogether 210 domestic and international trainee language teachers, enrolled on different university programmes, participated in the survey. The mean age of the respondents was 26.54 years (range – 18 – 57). The demographic characteristics of the participants are presented in the table below.

Self-reported data on the respondents’ skills and competence in the three different areas outlined above were analyzed using the SPSS statistical package. Overall, results showed that trainee teachers had a relatively high proficiency level in English (mean 3.70, std deviation 0.81). Similarly, they self-assessed their teaching competence as fairly strong (mean 3.15, std deviation 0.96). The lowest scores were reported for using AI tools (mean 2.84, std deviation 1.05).

Pearson correlation analysis was then performed to test if there were meaningful correlations between the three variables. The analysis revealed statistically significant correlations between them. Tabular presentation of the results follows.

**Correlation is significant at the 0.01 level (2-tailed).

*Correlation is significant at the 0.05 level (2-tailed).

The results from the analysis lent support to our assumption that all three variables would be meaningfully related as visible from Table 2. Respondents who exhibited higher abilities to use AI tools displayed higher levels of language fluency and better competence to teach preschool and primary school children.

Our next task was to explore if the participants could differentiate between human-authored and AI-generated stories. Not surprisingly, more than half of them (56.0%) could not correctly identify the origin of the two narratives. This result is congruent with data from previous research on the matter (Clark et al., 2021; Köbis & Mossink, 2020; Shah, 2023), claiming that the fluency of AI textual output renders it difficult to differentiate from human-authored texts.

Irrespective of the great number of respondents who were unable to identify the AI-generated text, most trainee teachers exhibited fairly high levels of confidence in their suppositions. Data showed that the majority of the participants were either very confident (24.1%) or moderately confident (40.2%) in having managed to recognise the origin of the two texts. Only a small percent (13.2%) were not at all confident in their guesses. The rest of the participants, representing 22.4 % of the group, expressed a weak degree of confidence.

The core aim of our survey was to gain insight into trainee teachers’ preferences for the two types of stories as well as their perceptions of the stories’ appeal for young language learners. Thus, the respondents were invited to read and offer their feedback on the two stories included in the survey instrument.

Regarding the participants’ own preferences for one or the other story and their predictions of children’s likes and dislikes, results were rather interesting. Meaningful correlations were found between respondents’ expressed preferences for a particular story and their AI skills. Trainee teachers with higher AI skills reported preference for either both stories (mean 3.17; std. 0.983) or the human-authored text (mean 3.07; std. 1.031) whereas the AI-generated story was more to the liking of respondents with lower skills in using AI forms (mean 2.69; std. 1.045). This group difference proved significant (F=3.381, df =2, p=0.04).

In general, the majority of the participants (nearly 60.0%) preferred the AI-generated story, yet 51.0% believed that children would like the human-authored story best. These findings are graphically presented below.

To provide insight into the rationale behind the trainee teachers’ own preferences and projections about young learners’ preferences, we offer some extracts culled from the participants’ comments on the two stories.

Trainee teachers’ own preferences as indicative of how they engage with either narrative text

Rationale behind engaging better with the human-authored story: “It is truly engaging, with its adventurous spirit and whimsical elements like the talking parrot. It feels more like a playful fairy tale, which makes it charming. I also found the ending more meaningful” (participant No 17).

Rationale behind engaging better with the AI-generated story: “I like both stories, but I find this story more appealing because of its message at the end. It emphasizes that the journey and adventure matter more than the destination, and it can teach children this valuable lesson” (participant No 208).

The above randomly selected responses of study participants point to a clear plot and overall meaning-related engagement with the narrative texts. The language of the stories was not acknowledged as a factor that influenced the overall preference for the narrative content.

Trainee teachers’ projections on which narrative text would be more appealing to young language learners as indicative of their potential to trigger situational interest

Rationale behind projected children’s better engagement with the humanauthored story: “Children will like this story better because it will be easier for them to understand the language and it is more interesting. It is very useful for developing communicative skills. It’s so easy for young learners to understand the whole story from the map, going through different locations and then reaching the treasure especially with the presence of a talking parrot giving them the opportunity to develop their language” (participant No 120).

Rationale behind projected children’s better engagement with the AI-generated story: “It will appeal more to children because it involves teamwork, a colourful parrot and an adventurous setting which children find exciting and relatable” (participant No 21).

Again, the two randomly selected responses suggest that the trainee language teachers have developed the ability to identify aspects of narrative texts that would activate situational interest in young learners of English. Furthermore, the focus on specific merits of the two stories, related to the development of particular linguistic skills in young learners, suggests that novice teachers are fairly capable of identifying language learning potential in narrative materials for foreign language instruction. In line with modern language learning pedagogies that strive to engage the entire personality of the young learner (Moskovkin & Shamonina, 2020), the participants’ responses highlighted possible benefits for children’s social and emotional development as well. Additionally, the fact that the respondents view texts differently, depending on whether they consider their personal preferences or the presumed preferences of young language learners, suggests a fairly welldeveloped ability to consider the usability of textual material in language learning from the perspective of learners’ engagement.

Participants’ responses, related to their own preferences and projections about children’s preferences for the two stories, merit a separate qualitative analysis. It is worth mentioning here that the trainee teachers have found similar tempting ideas and elements in both stories, such as adventure-based plot, friendship, the allure of talking animals, the overall moral messages, etc.

Finally, we performed a series of Analysis of variance (ANOVA) tests to check possible relations between the different demographic characteristics of the participants in the survey, their preferences for one or the other story, and their abilities to recognize the origins of the two stories. The studied variables (age, gender, nationality, degree programme) appeared unrelated to the respondents’ choice of texts. Similarly, all these subject variables bore no relation to the capability to identify AI-generated texts.

2.4. Discussion

The main focus of the present study was to explore how trainee language teachers perceive the usability of both story types for engaging young learners. Efficient language teaching approaches and techniques strive to trigger and maintain children’s situational interest by selecting materials which appeal both cognitively and affectively (Cameron, 2001; Cameron & Mckay, 2010). The study findings, related to research questions Q3 and Q4 (the extent to which young learners and trainee teachers would engage with the two narratives), indicate that the potential of the human-authored story and the AI-generated one to captivate young language learners is comparable, with a slightly greater weight being given to the humanwritten text as seen in Figure 2 above. This result gains additional value when analyzed in the context of respondents’ personal preferences for either story, where a greater extent of disparity, favouring the AI text (see Figure 1), was observed.

The pedagogical usability of both stories for early language learning is further highlighted by the higher percentage of respondents who perceived the two texts as equally fascinating for young audiences in comparison to the percentage of respondents who viewed the two texts as equally intriguing from their personal, adult perspective.

Another thought-provoking finding is the fact that study participants do not project their preferences onto the assumed children’s preferences for stories in the foreign language. Our interpretation of this result would be in the light of trainee teachers’ pedagogical competence to shift their mental perspective and imagine how young language learners, as intended target audience of the stories, would respond to the two narratives. We also find this result relevant to the understanding that in a learner-oriented language classroom “EFL teachers need to fully understand the complex and dynamic relationship and interplay between texts, readers and tasks they are intended to perform” (Chodkiewicz, 2016, p. 10). Additionally, our study also provides evidence that trainee teachers have acquired the pedagogical skill to identify materials that would spark situational interest in young language learners, although in varying degrees.

The study confirmed strong relations between trainee teachers’ English proficiency level, AI usage skills and teaching competence. These findings are pertinent to research question Q1. Respondents’ overt preference for AI-based stories over human-based ones, and their inability to differentiate between the two types, are not surprising. This finding, referring to research question Q2, is congruent to evidence from other research (Clark et al., 2021; Shah, 2023). We interpret this result as a natural outcome of reading habits, focused on content rather than on the specific linguistic means employed to convey meaning. This interpretation seems to be consistent with the results related to participants’ preferences for either story which clearly indicate that the content of AI-authored texts can also be narratively engaging.

Last but not least, among the participants in this study, it was those with lower AI skills who favoured the AI-based story compared to their peers with higher abilities to use AI tools and forms. Furthermore, findings, relevant to research question Q5, indicated no relation between studied demographic characteristics with the preferences of either type of story.

Conclusion

Integrating AI tools or their output in language education is a fairly new development (Binder, 2022; Kohnke et al., 2023). The present study explores a less-researched perspective on the potential of AI-written stories to capture the interest of young learners and be effectively used in early language learning. The findings indicate that trainee language teachers perceive AI-generated narratives as holding a fairly strong appeal for young minds and being conductive to the promotion of various linguistic skills along with affective and social capabilities. In addition, novice teachers approach the comparison between their personal textual preferences and the estimated children’s inclinations in an objective manner.

We believe that the implications of these results for the instruction of future language teachers are twofold. Firstly, interventions need to employ an even stronger emphasis on the mechanisms of activating and sustaining situational interest in young learners in line with holistic approaches to early language teaching (Cameron, 2001; Tomlinson, 2011). Secondly, the instruction of future teachers of languages can benefit from the formulation of specific criteria for the selection of teaching materials, thus empowering language teaching practice with the flexibility to cater for the various learners’ needs.

It would be appropriate to acknowledge the limitations of the present study. In spite of the reasonably large population sample, the convenience type of sample limits the generalizability of the findings.

Further research into early language educational practices on a national and international level can reveal other important insights into the efficient incorporation of AI models and AI-generated output. How effective language instructors can be in meeting the challenges posed by new technological developments will depend on their dedication to provide learning experiences which bring substantial cognitive, emotional and socio-cultural benefits for young learners.

Acknowledgements and Funding

This study is partially financed by the European Union – NextGenerationEU, through the National Recovery and Resilience Plan of the Republic of Bulgaria, project SUMMIT № BG-RRP-2.004-0008-3.3.

The authors express their gratitude to the participants in this study.

In line with the purposes of the study, ChatGPT-3.5 was used to generate a short story for children. No other part of the manuscript has been generated or enhanced by AI tools.

NOTES

1. British Council: LearnEnglish Kids (ca. 2018). The treasure map. https:// learnenglishkids.britishcouncil.org/listen-watch/short-stories/treasuremap

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