The world is changing rapidly, and technology is playing a major role in that change. With each passing year, new technological advancements are being made, and the world is becoming more connected and more automated.

       One major trend in future technology development is the continued growth of artificial intelligence (AI). As machines become more intelligent, they will be able to take on increasingly complex tasks, from driving cars to diagnosing medical conditions. However, the development of AI also raises ethical concerns, as machines become more autonomous and potentially pose a threat to human employment and safety. Another area of future technology development is the Internet of Things (IoT), which involves the connection of everyday objects to the internet. This can enable devices to communicate with each other and with users, leading to increased efficiency and convenience in our daily lives. However, the widespread use of IoT also raises concerns about privacy and security. In the future, it is also likely that virtual and augmented reality technologies will become more prevalent. These technologies can be used for a variety of purposes, from entertainment to education and training. However, they also raise concerns  about addiction and the potential for users to become detached from reality. Another area of future technology development is nanotechnology, which involves the manipulation of matter on an extremely small scale. Nanotechnology has the potential to revolutionize fields such as medicine, energy, and materials science. However, there are also concerns about the environmental and health risks associated with the use of nanomaterials. In the future, it is also likely that blockchain technology will become more widespread. Blockchain is a decentralized ledger system that can be used to securely store and transfer data and assets. But this technology has the potential to disrupt industries such as finance, healthcare, and supply chain management.

       Finally, advances in biotechnology may lead to new treatments and therapies for a wide range of diseases and conditions. For example, gene editing technologies such as CRISPR have the potential to cure genetic diseases, while stem cell therapies may one day be used to regenerate damaged tissues and organs.

       The notion of future L2 selves has emerged over the last decade as the dominant motivational perspective within the field of second language acquisition since the introduction of Dörnyei’s (2005, 2009) L2 motivational self-system (L2MSS). Future L2 selves represent the kind of L2 user that a learner wants to be in future. These future L2 selves act as desirable end-states that motivate learners to invest in learning a language. The desire to reduce the discrepancy between one’s actual L2 self, representing their current level of L2 abilities, and their desired future self, creates feelings of discomfort that motivate the learner to work hard and approach their future L2 self. Two general types of future L2 selves have been proposed in the field of second language acquisition (SLA). These include ideal L2 selves and ought L2 selves. Ideal L2 selves represent the ideal image of the kind of L2 user one (or their significant others) wants to be in future (e.g., a fluent speaker of the target language) and ought L2 selves represent the kind of L2 user that a learner believes they have to be in order to meet their obligations and responsibilities and avoid negative consequences (e.g., failing in school or losing one’s job).

       In another study, Papi et al. (2019) re-operationalized and tested a 2 × 2 model of L2 selves using data collected from 257 international students learning English as a second language (ESL) in the United States. The results of confirmatory factor analysis supported the existence of four future L2 selves from two regulatory dimensions (ideal vs. ought) and two standpoints (own vs. others). In other words, the confirmatory factor analysis confirmed the construct validity of the model with four future selves: Ideal L2 self/own representing a learner’s own hopes and aspirations for language learning； ideal L2 self/other representing a learner’s significant others’ (e.g., parents) hopes and aspiration for him or her in relation to learning the L2； ought L2 self/own representing a learner’s self-selected obligations and requirements in terms of language learning； and finally, ought L2 self/other representing the L2 learning obligations that a learner believes his or her significant others’ impose on him or her. In addition, and contrary to the results of previous studies, multiple regression analyses revealed that ought L2 self/own was the strongest predictor of students’ motivated behavior among the four types of L2 selves in the ESL context of the United States. Moreover, whereas ideal L2 self/own led to an eager strategic inclination for maximal L2 use, ought L2 self/own led to a vigilant strategic inclination for minimal L2 use. The authors argued that the ideal vs. ought future selves not only lead to qualitatively different motivated behaviors but also to different emotional responses during the learning pursuit, connections that have also been confirmed in two other studies using the same model (from Tahmouresi & Papi, 2021).