In a fast-changing world, education and training become increasingly important. For individuals, to “construct” their lives, all along from childhood to old age. For corporations, to be competitive. For communities, at all levels, to enable effective member participation.

To make education a global public goods is most important human endeavours, and one of the Sustainable Development Goals of the United Nations. Education and training are amongst the most essential collaborative activities. We collaborate to learn, and we learn to collaborate, all the time. If we have a closer look at the activities / learning modes in education and training, we witness a series of small activities, such as creating or selecting pedagogic contents, such as a presentation to attend, a text / book to read, a video to watch, an audio content to listen to, a game to play…); sharing / delivering these contents; training specific skills by varying exercises in similar situations; evaluating competencies; diagnosing learning difficulties / abilities to adjust pedagogical content; coaching students to encourage them, correct them, guide them…. All these activities require skills, which are unevenly distributed between teachers, and very individual teachers (very) rarely have at the same time, not to mention that in the current modus they lack sufficient time and energy to dedicate sufficient time to all of those as well as access to contents and tools, allowing them to carry them out at the best level.

Education and training are supposedly universal rights and key to the reduction of inequalities. Albeit, in spite of a lot of progress, education and training often do not deliver on these promises. High-quality education is increasingly a privilege of the elite, rather than the big equaliser that helps to overcome poverty and existential fear. This is related in particular to the fact, that customised high-quality education, adapted to individuals’ abilities and disabilities, as well as their past educational history, is still largely elusive. The default standard is mass education / training of “average” (and sometimes relatively mediocre) quality. Of course, there are self-training software contents, but they are relatively disconnected from effective coaching.

At SymPlace, we see education and training as complex sets of interconnected symFlos, combining different activities. Pedagogy can be understood at the same time as symFlo templates and as metaSymFlos corresponding to the process of structuring educational symFlos. Shareable symFlo templates become reusable and improvable pedagogic content, which can be employed by symBots to reduce the “collaboration overhead” of education and training, and thus enable new pedagogical practices. From this perspective, the quality of pedagogy, which is key to the success of education / training, lies not only in the contents of the individual activities, but at least as much in how we structure these symFlos: notably, in terms of chronology; of combining of individual work (“slow collaboration”) with group work (“fast collaboration”); of form. rhythm and pace of feedback.

ALC offers the opportunity of improving existing pedagogical contents, by making the corresponding collaborative flows explicit and improving them, by combining different learning modes in effective ways, and evaluating them, correlating the respective template, with student results and student profiles (past learning history). In other terms by closing learning cycles (teaching – learning – evaluating – adjusting – teaching) more rapidly, efficiently, and effectively, learning processes are thus catalysed. SymBots can orchestrate and facilitate the learning / collaboration processes and thus improve their effectiveness (the quality of learning) and their efficiency (a better usage of expensive resources to be allocated). Learning scripts have been increasingly deployed to guide teachers. For Sym, the first teacher is the pupil / student her.himself. With ALC learning scripts will be student-centred and allow the combination of symBots providing computer-aided education, with timely involved teachers, coaches and third parties, including parents and family. The possibility to add commercial micro-transactions to symFlos enables people to buy education and training from others, and to pay for contents which may have been work intensive to develop. Such “payments” can take place using monetary currencies but also virtual tokens (with the selfSymFlo of the supporting symBot acting as a dedicated blockchain)

But the potential of ALC goes much further.

Feedback loops are at the core of learning; our brain learns through feedback.  Even so, feedbacks in current pedagogical practice (from kindergarten to higher education) are the poor parent of education. There are many reasons for this sad state of affairs. In mass education (the typical one-teacher-for-many-students classroom) teachers and tutors simply have no or very little time and / or cognitive bandwidth for providing effective feedback and guiding students in making use of it. In one-to-one tutoring / coaching, tutors / coaches are in most cases underequipped to diagnose specific learning disabilities, and in particular deficiencies in the competence scaffold (i.e., the architecture of competencies), and to design effective learning strategies. To start with, typical context dependent questions will very much determine the about the appropriate feedback. How far does the specific form of feedback motivate students to overcome difficulties / to continue progressing beyond the basic requirements? To which extent do students benefit from the necessary level of individual guidance and support to overcome their difficulties / fully leverage their potential? These are intrinsically complex questions, which require to look at the educational symFlos, both the selfSymFlo of the student and the symFlos between students, teachers, tutors…

ALC will enable new forms of pedagogic content in the form of (bundles) symFlo templates combining new contents, specifically designed feedback loops focusing on specific learning abilities and disabilities, the coaching of individual learning pathways, the work in (alternatively homogeneous and heterogeneous) student-groups and interactions in larger groups. This will enable a much more differentiated approach to learning and coaching. More advanced students can be involved as coaches for beginners. Life-long learning could become much more effective.

SymBots can correlate results supplied by “student testing” symBots with student profiles and history (stored in the students’ selfSymFlos by their VPAs). Data sciences can be incorporated into symFlos to match and customise pedagogical content with realities at individual student and student group levels. ALC makes it possible to diagnose the students’ learning obstacles / facilitators, and then use this information to design effective curricula and / or to timely recruit and involve coaches / teachers to guide / help / encourage. SymBots, incorporating data sciences, can help users (teachers and / or students) to identify and customise student specific curricula as symFlos. Machine learning can derive from the resulting data the best pedagogical symFlos for a student to progress at the right speed to maintain “flow” (according to Csikszentmihalyi essential for motivation and performance of students); also, it can ensure that student-teachers can intervene to coach “their” protegees at the right moment.

The Extended Identity will enable the possibility to trace and monetise the participation of students, their coaches, teachers and pedagogical designers. It also provides support for a more differentiated approach to competence certification, which can be used for proving competence to employers, clients, academic institutions, and partners. ALC also enables new forms of regular competence certification updates, carried out at low cost by such student-teacher competence communities. Thereby, competence communities could guarantee the skills of members as well as their capacity to transmit them further.

The combination of these features opens entirely new perspectives in education. In an increasing number of countries, qualified sufficiently trained teachers are a scarce resource. SymCommunities can be used to organise students-teachers-coaches in new ways. One of the visions of “Sym” is to enable an Open Education Society offering high-quality education and training as a “general public common good” based on an economic model based on “marginal costs” (i.e., where only incremental costs of new students and contents need to be financed). State-of-the-art translation facilities (e.g., enabled by LLM) make it possible to enlarge competence communities beyond the borders of schools, regions, or countries. ALC enables a concrete implementation of this model of competence communities of student-teachers, in which students are expected to develop not only skills but also to learn to coach and / or teach others in these skills, and to offer a minimal quantity of these services for free to the competence community, to ensure sufficient trainer resources. The more people participate in a competence community, the deeper and broader the available base of pedagogical collaborative contents and reference data will be. Free-of-charge content and tutoring can then be complemented by commercial pedagogical contents (in particular those require significant investments) which can be marketed, e.g., through the future SymPlace MarketPlace. This can make it possible to bridge a future “non-governmental” free-education-and-training domain with a commercial-private-education “market”, both possibly cross-fertilising each other, economically (with paid for services funding free services) and content-wise (with a broader base for generating content and learning how to use it for which students).

One of the ventures supported by the SymCubator (the venture incubator which also helped to formalise the concept of SymPlace and ALC, see below for more information), is the SymUniversity, which would provide the technological, social, and institutional platform for a network of competence communities, (ultimately) covering all ages and competencies.

Two examples of future symBots and symFlos:

Our first example is an “integrated” course on “business planning” (or any other “complex” subject), with a series of modules, organised as a “scaffold” organising pedagogic modules by required competencies for building “complex” competencies from more “basic” ones, and combining some or all of the following in different sequences:

• The “BP Course” symBot starts a quick test (specialised symBot) to check if the student needs to study / train with the material in the pedagogic module.
• It then runs a few quick tests (specialised symBots) to check if the student disposes of the necessary underlying skills needed to follow successfully the course; it then makes a recommendation to the student; if there is doubt and / or disagreement, it organises a meeting with a course manager to take a final decision on course participation.
• It then shares (via another specialised symBot) high-quality video content on the relevance and intrinsic interest of the course; at the end, it collects feedback on the course.
• This is then followed by another high-quality video content (another specialised symBot) to explain the basic concepts (and again collect feedback).
• The “BP Course” symBot then calls upon a specialised symBot running quizzes to test understanding / diagnose misunderstandings.
• Students can raise questions about the content and to receive answers from fellow students; students providing effective help get “rewarded”; student-tutors get backed up with “Quality Assurance” from fellow students, teachers and specialised symBots.
• They can, in parallel, informally chat about the course, and organise, on the spot, related videoconferences; a dedicated symBot can generate, after the session, a summary of highlights.
• They participate in more complex exercises for self-training, with feedback from tutors and / or fellow students.
• They then play a third-party business game (the API is embedded in a symBot) where participants get organised in competing blue and red teams (blue teams to create business models, red teams to evaluate them), and an information market including all participants and external experts (the corresponding processes for challenging an evaluating are supported by dedicated symBots.
• Students participate in formal tests, which serve the purpose of competence certification.
• An aggregating symBot pulls all the results together to decide on the delivery of the certification.
• Throughout the course, metaSymFlos provide help on the training course, and collect feedback to improve it.

Our second example is a “Math Team Olympiad” for children in primary school, where teams composed of kids with different abilities train each other on different maths skills and then participate in a series of tests. The teams are rewarded for their ability to make progress team members with specific learning difficulties, by mutual coaching. Groups are regularly recomposed to provide the opportunity to benefit from learning transfer, and to make emerge participants who are particularly good at coaching others.

When the author of this white paper asked ChatGPT 4.0 for suggestion, he was amazed of how well it captured the essence of ALC’s potential for education:

Author of this FAQ: “Could you please provide examples of transformative symFlos and symBots in education?

ChatGPT: Certainly! In the field of education, Augmented Learning Collaboration (ALC) can introduce transformative symFlos and symBots that enhance collaboration and learning experiences. Here are a few examples:

• Collaborative Learning SymFlo: A symFlo template designed to facilitate collaborative learning among students. It can include features such as virtual discussion boards, shared document editing, real-time feedback mechanisms, and automated knowledge assessment. The symBot associated with this symFlo can help students in managing their collaborative projects, providing suggestions for effective teamwork, and offering personalized learning resources based on individual needs.
• Personalized Tutoring SymBot: This symBot acts as a virtual private assistant for students, supporting their individual learning journeys. It can provide personalized recommendations for study materials, track progress, offer explanations and examples when students face challenges, and adapt the learning experience based on each student's strengths and weaknesses. The symBot can leverage machine learning algorithms and adaptive technologies to create tailored learning paths.
• Virtual Classroom SymFlo: A symFlo designed to create an immersive virtual classroom environment, allowing students and teachers to interact and collaborate seamlessly regardless of their physical locations. It can incorporate features like video conferencing, virtual whiteboards, synchronized content sharing, and real-time collaboration tools. The symBot associated with this symFlo can assist in managing class schedules, organizing resources, facilitating discussions, and providing on-demand support to both students and teachers.
• Collaborative Research SymFlo: This symFlo template is geared towards promoting collaborative research projects among students and educators. It can include features such as shared databases, collaborative data analysis tools, project management dashboards, and communication channels for effective coordination. The symBot associated with this symFlo can assist in locating relevant research articles, suggesting methodologies, facilitating peer review processes, and supporting knowledge dissemination.
• Lifelong Learning SymBot: This symBot focuses on fostering a culture of lifelong learning by providing personalized recommendations for continuous skill development and knowledge acquisition. It can suggest relevant courses, workshops, and learning resources based on an individual's interests, career goals, and learning history. The symBot can also support the creation of learning communities, connecting learners with similar interests and facilitating knowledge sharing.

These are just a few examples of how symFlos and symBots in ALC can transform education by enhancing collaboration, personalization, and learning outcomes. The specific applications and functionalities of symFlos and symBots can be tailored to different educational contexts and requirements.

And when requested to propose more, ChatGPT came up with more interesting suggestions:

• Personalized Learning Paths: ALC can provide personalized learning paths for students based on their individual needs, interests, and learning styles. SymBots can analyze data on student performance, preferences, and progress to offer tailored recommendations for learning resources, activities, and assessments. This personalized approach enhances student engagement, motivation, and overall learning outcomes.
• Global Classroom Connections: ALC can connect classrooms worldwide, enabling students to collaborate with peers from different countries and cultures. SymFlos can facilitate cross-cultural projects, virtual exchanges, and joint learning initiatives. This exposure to diverse perspectives fosters global citizenship, intercultural competence, and empathy among students.
• Accessibility and Inclusion: ALC promotes accessibility and inclusion in education. SymBots can provide assistive technologies, such as text-to-speech or speech recognition, to support students with disabilities or learning difficulties. Collaborative symFlos can facilitate inclusive group work, ensuring that all students can actively participate and contribute to collaborative activities.
• Lifelong Learning and Professional Development: ALC extends beyond traditional educational settings to support lifelong learning and professional development. SymBots can assist individuals in identifying skills gaps, suggesting relevant courses or training programs, and tracking progress towards career goals. Collaborative symFlos can create networks of professionals, allowing for knowledge sharing, mentorship, and continuous learning.
• Data-Driven Decision Making: ALC leverages data analytics to inform educational decision making. SymBots can collect and analyze data on student performance, engagement, and behavior to provide insights to educators. This data-driven approach enables targeted interventions, adaptive instruction, and evidence-based practices to improve teaching and learning outcomes.
• Gamified Learning Experiences: ALC incorporates gamification elements to enhance student engagement and motivation. SymFlos can introduce game-based activities, challenges, and rewards to make learning more interactive and enjoyable. SymBots can provide real-time feedback and progress tracking, transforming the learning process into a gamified and immersive experience.
• Collaborative Problem Solving: ALC emphasizes collaborative problem-solving skills. SymFlos can facilitate project-based learning, where students work in teams to tackle real-world challenges. SymBots can assist in coordinating group tasks, facilitating communication, and providing guidance throughout the problem-solving process.
• Teacher Professional Collaboration: ALC supports collaboration among educators, enabling them to share best practices, resources, and instructional strategies. Collaborative symFlos can foster professional learning communities, where teachers can collaborate, co-plan lessons, and provide peer feedback. SymBots can assist in curating and organizing resources, recommending professional development opportunities, and facilitating teacher collaboration.

Inversely, education and training are critical to the success of ALC. To develop new individual collaboration habits and collective collaboration practices, requires continuous learning. SymFlo templates must embed the pedagogy to learn how to participate in the corresponding complex collaborative activities. Effective new ALC-solutions must embed corresponding meta-ALC for learning, coaching, and teaching. The more effectively this is done, the better the user experience and the faster the take-up of ALC – ALC’s success is directly correlated with the ability of individuals and groups to learn how to use it to their benefits. Also, advanced AI technologies for education and training offer great potential for learning how to combine ALC with LLM to generate customised collaborative pathways in complex fields such as education and health. Furthermore, education and training come with the challenge of helping users to find and select the pedagogical content (i.e., symFlo templates) that is best adapted to their learning abilities / disabilities; again, this know-how will be very useful when it comes to helping users choose the symFlo templates / symBots depending on their needs, capabilities and preferences.

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