Alongside advances in artificial intelligence and big data analytics, the affordances of digital technologies related to mobile-assisted language learning, virtual reality, and game-based language learning have inspired significant innovations in language learning, where learning is no longer isolated from the environment and restricted inside classrooms. However, empirical practices and theoretical discussions have lagged behind the latest developments in digital technologies. Therefore, formal theoretical synthesis and an analytical framework of digital language learning (DLL) are required to answer the questions of why and how recent digital tools could benefit language teachers and students (Li & Lan, Reference Li and Lan2021).

DLL tools and platforms embed a high level of interactivity, which allows users to interact with real-life or real-life-like contexts and with other users, resulting in deeper cognitive processing and rich social learning. This enabled interactivity also facilitates the realization of the key affordance of autonomy in social learning, in which students can decide on what, when, where, and how to learn. After analyzing the interactions of individual learners with digital tools and platforms assisted by big-data analytics, personalized learning materials and methods can be adopted to fit individual learning profiles. During user-tool interactions, DLL tools and platforms could continuously provide affective and emotional feedback to student activities (such as virtual human faces displaying different emotional expressions) that could attract learner attention; they comprise an important factor for successful language learning. In addition, it appears that the broader brain network in cortical, subcortical, and limbic systems supported by DLL can result in positive phenomena in other dimensions, such as enhanced cognitive processing and long-term memory retention. Thus, discussing the cognitive, social, affective, and neutral dimensions of DLL can facilitate researchers’ understanding of how digital technologies innovate in language learning.

Although DLL holds great potential to enhance both child L1 and adult L2 learning, moving forward will require researchers to collect more empirical evidence and conduct theoretical discussions of this newly emerged language learning method. Increased attention should be paid to differences among individuals or groups of leaners. For example, evidence should be collected of how DLL interventions could benefit high-performance young students. In addition, evidence has revealed that individual performances can vary according to some learner characteristics, such as spatial abilities (Li, Legault, Klippel & Zhao, Reference Li, Legault, Klippel and Zhao2020). Thus, it is essential to identify how DLL could facilitate learners with lower spatial abilities. Moreover, relatively few digital tools or platforms have been designed for advanced learners. Therefore, further attempts at incorporating big-data analytics in digital tools are required to facilitate the language learning of those learners with very high language competence. In this regard, we suggest that corpora with large amounts of authentic language data should be integrated into DLL tools and platforms to provide personalized and suitable data for intelligent language learning, especially for learners with different proficiency levels.

A number of DLL tools and platforms have been found to aid second language learning, yet the practical incorporation of DLL into second language teaching should be fully discussed. The abundant research evidence can help teachers to perceive the new learning and teaching technologies as useful. However, teachers may not be concerned with learning about and introducing these tools and platforms to their students. This is particularly true if teachers consider the technologies too difficult to implement in classrooms or if they lack the relevant and necessary pedagogical abilities to incorporate such technologies (Ma & Chiu, Reference Ma and Chiu2021). In addition, to demonstrate the effectiveness of digital platforms in language learning and teaching further, it will be crucial to train teachers regarding how to use such platforms for designing technologically based curricula (Chun, Reference Chun2019). It is suggested to investigate experimental engagement and actual practices of teachers with the new digital technologies in classrooms, to collect their opinions on involving technologies in their curricula. Another important issue we need to consider is how language teachers could introduce DLL tools to their students and guide them to interact with digital devices in formal or informal settings. Thus, we should pay more attention to teacher education to facilitate the implementation of DDL in classrooms. Further, it is the acceptance and willingness of teachers to integrate DLL into their teaching that will eventually benefit a vast number of students.

It is also recommended to explore the relationship between and the co-performance of different dimensions in more detail. For example, we should investigate how sufficient affective representations of digital tools and platforms facilitate higher learner autonomy and deeper cognitive processing; or how neural networks that underlie social learning can be enhanced by digital technologies. In addition, abundant empirical evidence has been collected regarding learner development after a short period (a few weeks or months) of DLL intervention. Although learners’ cognitive abilities and motivational factors changes diachronically, it remains unclear how DLL could affect learners’ language learning over a longer period and whether learners use digital technologies autonomously for learning purposes after being introduced to digital tools and platforms.

In conclusion, DLL serves as an important theoretical and empirical representation of how advanced digital technologies function in language learning research and practices. Its analytical framework allows us to consider the crucial factors that contribute to successful language learning assisted (or enhanced) by such technologies. It is also hoped that increased empirical evidence will be collected in future research to test and enrich DLL.