دانلود مقاله بازنگری در آمیختگی بین هوش مصنوعی و یادگیری انسان
چکیده
1. مقدمه
2. تعریف قلمرو
3. روش شناسی
4. نتایج
5. بحث مشترک
6. سخنان پایانی
اعلامیه منافع رقابتی
سپاسگزاریها
منابع
Abstract
1. Introduction
2. Defining the territory
3. Methodology
4. Results
5. Joint discussion
6. Concluding remarks
Declaration of competing interest
Acknowledgements
References
چکیده
تکثیر هوش مصنوعی در بسیاری از جنبه‌های زندگی انسان - از اوقات فراغت شخصی، کار حرفه‌ای مشترک، تا تصمیم‌گیری‌های سیاستی جهانی- سؤالی جدی در مورد چگونگی آماده‌سازی مردم برای جهانی به‌هم‌پیوسته و در حال تغییر که به طور فزاینده‌ای از دستگاه‌های فناوری و عوامل اشباع می‌شود، ایجاد می‌کند. ماشین آلات در دنیایی که هوش مصنوعی دارد، مردم به چه نوع قابلیت هایی نیاز دارند؟ چگونه می توانیم این قابلیت ها را مفهوم سازی کنیم؟ چگونه می توانیم به زبان آموزان کمک کنیم تا آنها را توسعه دهند؟ چگونه می توانیم توسعه آنها را به صورت تجربی مطالعه و ارزیابی کنیم؟ با این مقاله، بحث را با اتخاذ رویکرد دانش‌سازی گفتگوی باز می‌کنیم. تیم ما متشکل از 11 نویسنده مشترک در یک بحث کتبی هماهنگ شرکت کردند. با درگیر شدن در یک گفتگوی مکتوب نیمه مستقل و نیمه مشترک، مجموعه‌ای از ایده‌ها را در مورد اینکه این قابلیت‌ها چیست و چگونه می‌توان به زبان‌آموزان برای توسعه آنها کمک کرد، گردآوری کردیم. به طور همزمان، ایده‌های مفهومی و روش‌شناختی را مورد بحث قرار دادیم که ما را قادر می‌سازد دیدگاه‌های فرضی خود را آزمایش و اصلاح کنیم. در ترکیب این ایده‌ها، ما پیشنهاد می‌کنیم که نیاز به حرکت فراتر از دیدگاه‌های مبتنی بر هوش مصنوعی در مورد قابلیت‌ها و در نظر گرفتن بوم‌شناسی فناوری، شناخت، تعامل اجتماعی و ارزش‌ها وجود دارد.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
The proliferation of AI in many aspects of human life—from personal leisure, to collaborative professional work, to global policy decisions—poses a sharp question about how to prepare people for an interconnected, fast-changing world which is increasingly becoming saturated with technological devices and agentic machines. What kinds of capabilities do people need in a world infused with AI? How can we conceptualise these capabilities? How can we help learners develop them? How can we empirically study and assess their development? With this paper, we open the discussion by adopting a dialogical knowledge-making approach. Our team of 11 co-authors participated in an orchestrated written discussion. Engaging in a semi-independent and semi-joint written polylogue, we assembled a pool of ideas of what these capabilities are and how learners could be helped to develop them. Simultaneously, we discussed conceptual and methodological ideas that would enable us to test and refine our hypothetical views. In synthesising these ideas, we propose that there is a need to move beyond AI-centred views of capabilities and consider the ecology of technology, cognition, social interaction, and values.
Introduction
The appearance of computers in the workplaces at the turn of the 21st century has added ‘algorithmic thinking’ and ‘computing literacy’ to the repertoire of thinking skills and literacies that have been seen as essential for successful functioning and employment in society (Knuth, 1985; Papert, 1972; Sloan & Halaris, 1985). The proliferation of personal computers and other digital devices in people's everyday lives raised the need for different kinds of skills and literacies, such as ‘ICT skills’, ‘media literacy’ and ‘digital literacy’ (Markauskaite, 2005, 2006). The recent emergence of big data, machine learning, robotics and Al gave the birth to ‘data literacy’, ‘computational thinking’, ‘AI literacy’ and other new skills (Bull, Garofalo, & Hguyen, 2020; Long & Magerko, 2020; Mandinach & Gummer, 2013). Simultaneously, the increasing interconnectivity, complexity, and fast changes in knowledge and skills needed for everyday life and jobs have shifted the attention from technology-centred skills and literacies to a broader set of generic competencies, such as creativity, analytical thinking, active self-driven learning, and global citizenship (World Economic Forum, 2018, 2020).
Results
Our individual perspectives on the capabilities for an AI-infused world ranged from more individual, cognitively oriented views to more relational, socially oriented perspectives. We use this dimension as a guide to sequence our contributions, starting from the perspectives that emphasise individuals and moving towards broader, relational conceptualisations.
4.1. Using AI to become an agentic learner: A self-regulated learning perspective (Dragan Gašević, DG)
4.1.1. Q1: What kind of capabilities do people need in a world with Al?
Developments in AI accelerate technological change in workplaces and demands for continuous learning, upskilling, and reskilling. To maintain job relevance and support future career transitions in a world with AI, individuals will require highly developed self-regulated learning (SRL) skills (Winne et al., 2017). These are not just important for matters related to labour markets but also for other aspects of life such as personal finances, health, culture, and climate. SRL skills play a critical role in all facets of human learning and development. For instance, SRL underpins how learners navigate and operate on online information, form queries to search information on the Web or social media, and scan and assemble information. At each step, learners decide what information is relevant and judge how it supports achievement of their learning goals (Dunlosky & Thiede, 2013). The need for SRL skills is even more acutely emphasised in the age of AI due to two prominent reasons: (i) the need to adapt (re- or up-skill) frequently due to speed of job and life changes; and (ii) the need to maintain agency in decision making while working AI systems.