A Report on the Future of University English Education in Light of the Development of AI

 

Presented below is an English translation of the Japanese text that I prepared for my lecture at ELPA on June 19, 2021.

The translation process was as follows.

(1) I inputted the Japanese text with no particular pre-editing into DeepL. 

(2) I post-edited the English output from DeepL by myself. 

(3) I copied the revised English to Grammarly and edited it slightly more, following some of its suggestions.

The user experience was pleasant. Post-editing did not take much time because DeepL's output was satisfactory in most cases from my point of view. I saved a massive amount of time, which I would have needed without AI. Furthermore, I did not consult native English speakers. The experience may demonstrate that non-native English users benefit significantly from AI if they use it adequately, as I indicate below. AI may empower non-native English users to be more autonomous.

The PDF file is downloadable from here.

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A Report on the Future of University English Education

in Light of the Development of AI

 

 

 

Analogy

 

Adults use calculators and spreadsheet apps to perform complex calculations.

Few adults, however, allow children to use them from the start.

Children should develop a sense of number before using such convenient tools.

 

 

Hypothetical Question

 

What if calculators and spreadsheet apps were only right, say, 95% of the time?

 

 

 

0 Summary

 

Despite its remarkable progress, the current mainstream AI (Artificial Intelligence) has structural and functional limitations: it can assist and extend the intelligence of the individual who uses it, but it can never replace it. Therefore, for the use of English by non-native speakers, users need to judge the output of AI and make necessary corrections because AI is only a tool as an auxiliary and extended intelligence. For the area where AI is not of sufficient help, in particular, English language users must acquire English language skills in their flesh and blood. Therefore, future university English education, which must assume that graduates will use AI later in their career, should transform qualitatively and focus on teaching English skills that AI cannot aid, extend, or substitute human abilities. Universities should enable learners to use English after graduation by exploiting AI as a tool that can successfully assist and extend their physically embodied English skills.

 

The current paper presents the author’s proposal for the future of university English education (teaching academic English at a research-oriented university) based on a theoretical review of AI. The author believes that the proposal should be examined for its validity and feasibility by all those involved in English education (beneficiaries, instructors, policymakers) and that reform of English education, both drastic and cautious, should be undertaken.

 

 

1 Theoretical Review of AI

 

1.1 Three principles for the relationship between AI and humans

 

On the basis of the structural and functional limitations of AI described below, this paper argues that users should agree on the following three principles.

 

Principle 1: AI can assist and extend human intelligence, but it will not be a complete replacement.

 

Principle 2: Humans must judge and correct AI output when necessary.

 

Principle 3: Humans must take the initiative and responsibility for the use of AI.

 

 

1.2 Structural and functional limitations of AI

 

Structural Limitation

 

(1) Lack of a biological body filled with emotion: AI behavior is categorically different from human behavior that prioritizes life; AI possesses no biological body that produces "emotion" to sustain life and thrive.

 

(2) Lack of sufficient world models: Since AI has not gone through the process of evolution and selection, it has not internalized working models of the world for better chances of survival. Therefore, AI can only use the architecture and data provided by the programmer. Consequently, it cannot utilize models of the world to successfully learn and make inferences from a small amount of data, as humans do.

 

(3) Inability to understand meanings and stories: AI's perceptions are restricted to very limited distinctions. Thus, it cannot infer from its perception other diverse aspects of the object it recognizes (the "actuality" of meaning) or the numerous potential connections that the object could have (the "potentiality" of meaning), as humans do. Even when AI can recognize multiple objects simultaneously, it does not understand the mutual relationships they can possess. In other words, AI cannot understand in the form of a "story," which is a coherent constellation of various meanings.

 

(4) Inability to create new values and hypotheses: AI can only learn and reason about predetermined issues. AI cannot invent new and significant viewpoints (values) and conceptions (hypotheses) beyond the domain of those issues.

 

(5) No social communication: AI only learns as an individual entity and cannot maintain contingent correspondences (i.e., communication) with equal but different entities. It, therefore, does not experience unexpected qualitative transformations, as humans do.

 

Functional Limitation

 

(6) Weakness in long-tail phenomena: AI is weak in learning atypical and exceptional phenomena (i.e., cases that are few in number but exist in many kinds in the real world). AI often err on rare items in big data, including specialized knowledge.

 

(7) Mistakes that are unthinkable for humans: Because AI’s "understanding" is categorically different from human’s understanding, AI produces mistakes that humans do not usually predict.

 

(8) Only domain-specific learning and reasoning with no analogical application: Even when AI shows superhuman ability in a limited task, it does not possess reasonable ability in related domains other than that. AI is not flexible or versatile.

 

 

 

2 The Future of University English Education

 

2.1 General Discussion

 

From the structural and functional limitations described above, we may formulate the following guidelines for university English education in the future.

 

(1) Emphasizing emotional experiences through English: Instructors should not reduce English learning to a mere formal manipulation of signs. They should emphasize the emotions that arise in students through English and the reactions they initiate from those emotions, for emotions are the source of human cognition and behavior.

 

(2) Learning about the world through English: Instructors should recognize again that learning English is about learning about the world. They should choose English materials and learning tasks that have strong connections to the real world. The relationship between language and the world is one area in which AI is weak (AI processes language only as forms).

 

(3) Learning about the possibilities of English expressions: Learners should sufficiently understand the potentiality of meaning that an English expression can have in addition to its literal meaning (the limited meaning that even objective tests can determine). Learners should also be proficient in understanding and expressing meaning in a narrative form that integrates various meanings coherently. AI cannot constellate different meanings coherently.

 

(4) Emphasizing creative responses from understanding English: Even classes for receptive skills should not end with reading and listening; it should begin with reading and listening. Even classes for reading and listening should teach producing relevant responses that can result from that comprehension. AI can only process what it is designed to process, unable to respond creatively from that processing.

 

(5) Developing the ability to collaborate with multiple people in English: Learners should use English appropriately with numerous people with different backgrounds and understandings. Instructors should stop basing lessons solely on the evaluation of individual-based learning using uniform criteria. AI cannot perform social communication, i.e., the coordination of relationships among multiple individuals despite differences and contradictions.

 

(6) Emphasizing expressions about non-typical and exceptional matters: Students should enhance the learning of expressions that are “unusual” from the viewpoint of big data (e.g., technical terms and Japanese idioms), where AI tends to err; AI is often wrong about such long-tail phenomena.

 

(7) Learning about the mistakes that AI can make: Learners should learn to identify and correct unexpected mistakes that AI commits. They should be free from a widespread myth that AI is accurate and fair because it is a machine.

 

(8) Learning to respond flexibly: Learners should learn to invent ad hoc expressions by which humans communicate without knowing precise expressions. Humans should advance their flexible adaptability, which AI does not possess.

 

 

2.2 Writing

 

In Academic English writing, the following writing process will probably enhance non-native English users’ writing skills through AI. Therefore, in university English education, instructors should teach the writing strategies below. However, instructors need advanced knowledge and skills in both Japanese and English.

 

(1) Writing in Japanese: Learners can write the manuscript in Japanese, which allows them to organize and express their thoughts most precisely without losing concentration over a long time. However, additional guidance may be necessary for learners whose mother tongue is not Japanese.

 

(2) Pre-Editing: After learning conspicuous differences between Japanese and English, learners should revise the Japanese manuscript into machine-friendly Japanese to prevent errors in machine translation as much as possible in advance. However, critical consideration may be necessary about this kind of modification of Japanese into an English style from the perspective of linguistic and cultural diversity and the domination of English that suppresses it.

 

(3) Post-editing: Students need to revise the English output that machine translation produced. The revision requires a high level of English language ability to objectively read the output English from a third-party perspective and evaluate it stylistically.

 

 

2.3 Reading

 

As mentioned above, advanced reading skills are essential for AI-based writing. They are critical for learning the vocabulary necessary for listening and speaking (i.e., acquiring detailed knowledge of the collocational possibilities, which cannot be obtained by rote memorization of words). Reading instruction in the future requires more than a rough translation for approximate understanding, which machine translation achieves instantly. Specifically, instructors can consider the following four policies.

 

(1) Stylistically analytic reading: Students should read carefully selected English text, compare it with other possible expressions, and accurately understand the English text’s meaning. Since AI cannot understand the subtle possibilities of meaning, humans need to develop their skills in this stylistically analytic reading. This intensive analysis is also necessary for post-editing in writing.

 

(2) Psychosomatic expression in the style of reading theatre: Learners need to feel the sounds of the English text and the emotional vibrations they generate in their bodies. They should also read the text aloud themselves, resonating with their emotions in the manner of reading theatre. Since AI and robots have no biological body like humans’ and cannot express themselves emotionally, psychosomatic expression in the style of reading theatre may be crucial for English learners.

 

(3) Translation writing: Instructors should encourage students to write Japanese translations to know their understanding of the high-quality English texts they selected. Translation experience will help students gain a deeper and more accurate knowledge of both Japanese and English. Translation writing is also critical for identifying and correcting errors in AI translation in the post-editing process.

 

(4) Teaching with tasks and projects: The real value of English comprehension in the real world depends on what actions the reader can take from that comprehension. To make reading instruction "begin with reading" rather than "end with reading," instructors should integrate English reading into other meaningful tasks or projects. The successful use of language to complete tasks or projects is necessary for the development of the human capacity to connect symbolic understanding to real-world actions. AI cannot render symbolic processing into real-world actions because it only processes symbols as formal notations.

 

 

2.4 Speaking

 

Human speaking is not just a production of linguistic signs; it is an expression that involves the whole body's emotions. Humans express and understand each other not only through linguistic signs. They also communicate through all paralinguistic expressions (prosody, such as rhythm and intonation) and non-verbal expressions (eye contact, facial expressions, gestures, movements, among others) that emerge simultaneously. The AI/robot with no emotional body cannot perform this integrated task. Therefore, with regard to speaking, humans cannot expect much assistance or expansion from AI. Instructors must emphasize teaching speaking skills in the future.

Even when humans use AI to convey information that does not require much emotional expression or understanding, AI cannot accurately represent long-tail items such as genre-specific technical terms that academic English contains. Speaking instruction in university English education should aim at developing learner’s embodied speaking skills without the help of AI.

 

 

2.5 Listening

 

AI correctly recognizes suprasegmental sound changes (linking, reduction, assimilation, among others), which are features that many Japanese learners have not mastered. On the other hand, AI does not always successfully recognize specialized expressions, which are well-known to specialists but belong to the long tail in Big Data. Thus, while AI can help with teaching suprasegmental features, it cannot entirely replace human listening skills.

Listening is not limited to the reception of linguistic signs, either. It also includes the emotional responses that emerge in the listener’s body. The speaker observes those emotional expressions to judge whether the listener understands appropriately. Listening instructors must also attend to the listener's emotional response.

In addition, the understanding and embodiment of the sound features of English is critical are speaking. Speakers who acquired sound feature patterns can reproduce them appropriately when they express themselves in speaking. Speech with appropriate sound features definitely promotes better comprehension in listeners.

Those considerations above lead to the following two guiding principles.

 

(1) Listening experience until the sound features of English are embodied: The goal of listening instruction should not be limited to accurate recognitions of linguistic signs. It should include appropriate emotional response and, ultimately, the embodiment of sound features of English. The embodiment, in particular, requires analytical and conscious training because they are distinct from Japanese features. With sound features of English embodied, learners can express themselves far more effectively. Also, in silent reading, learners understand more comfortably if they can appropriately vocalize the printed text in their mind; silent reading is, after all, listening comprehension of sounds rendered from letters on the text. Writing, too, is producing texts that readers can comfortably vocalize in their minds. Therefore, mastering the sound features of English through listening helps learners to write better. Listening instruction should no longer be about receiving information and determining its correctness through multiple-choice tests.

 

(2) Selecting listening materials on the basis of individual learners' interests: The English subtitling function of the Chrome browser automatically recognizes and transcribes English audio on the Web. It converts English videos that most stimulate learners’ particular intellectual interests into learning materials with subtitles. (Imperfections in the subtitles need to be addressed, though.) Learners can turn the transcribing function on and off and change the difficulty of listening so that they can train themselves to improve their listening skills. Since learners are motivated by the material they choose, they more accurately perceive the subtle nuances of meaning expressed in its sound features. Students can repeat this meaningful listening experience abundantly until learners embody the sound features of English they hear.

 

 

3 In Closing

 

The direction of university English education described above obviously assumes that learners have acquired a certain level of English ability before they enter university. As an English instructor at university, it is difficult for me to predict how English education in elementary, junior high, and high schools will change with the development of AI. However, the review and analysis above suggest that English teaching before university should maintain the principles of "teaching and evaluating abilities students embody in their flesh and blood" and "fostering human-specific abilities.” The challenge for education in the future lies in developing human intelligence that coexists with AI. Because English education at and before university is closely related, those involved in university English education must continue to pay attention to the state of English education in elementary, junior high, and high schools.

 

The current report theoretically reviewed AI to propose some possibilities of the future English education at university (and below that level, briefly). As mentioned at the beginning, this report merely reflects the author’s consideration. The author hopes that this document will serve as a starting point for a deeper discussion on the future of English education.

 

 

  

References

 

瀧田寧・西島佑（編著） (2019) 『機械翻訳と未来社会』 社会評論社

藤本浩司・柴原一友 (2019a) 『AIにできること、できないこと』 日本評論社

藤本浩司・柴原一友 (2019b) 『続　AIにできること、できないこと』 日本評論社

松尾豊 (2015) 『人工知能は人間を超えるか ディープラーニングの先にあるもの』角川書店

松尾豊・塩野誠 (2016) 『人工知能はなぜ未来を変えるのか』角川書店

松尾豊 (2019) 「深層学習と人工物工学」 https://www.jstage.jst.go.jp/article/oukan/2019/0/2019_F-5-2/_pdf

松尾豊 (2020) 「人工知能　ディープラーニングの新展開」、西山圭太・松尾豊・小林慶一郎 (2020) 『相対化する知性』日本評論社 (pp. 1-103)

丸山宏 (2019) 「高次元科学への誘い」https://japan.cnet.com/blog/maruyama/2019/05/01/entry_30022958/

丸山宏 (2019) 「人工知能研究者として私たちがすべきこと」https://japan.cnet.com/blog/maruyama/2019/12/31/entry_30022985/

ミッチェル, M. 著、尼丁千津子訳 (2021) 『教養としてのAI講義』日経BP (Mitchell, M. (2020) Artificial Intelligence: A Guide for Thinking Humans. Pelican.)