Question
Asked 4 September 2023

Will the AI driven Humaniod Robots be able to replace Teachers at schools and eventually University Faculty & Professors while providing high quality?

Educators don’t need to worry about artificial intelligence taking over their jobs. While AI is becoming a valuable tool for educational professionals, there are many ways computers just can’t replace the human touch in the classroom.
Popular media is full of stories about technology’s potential to replace human workers, including teachers – but the truth is that there is more to teaching than simple knowledge transfer.
Machines can’t replace the human touch that is necessary in our schools that can only be delivered by high-quality educators.
Instead of feeling scared of AI, teachers and educational professionals can look at it as a powerful tool for delivering better, more personalized learning experiences and lifting some of the enormous administrative burden currently placed on educators’ shoulders.

All Answers (3)

Najla Matti Isaacc
University of Mosul
The potential for AI-driven humanoid robots to replace teachers at schools and university faculty and professors is a subject of debate and speculation. While AI and robotics have made significant advancements in various fields, including education, there are several factors to consider:
1. **Teaching Complexity**: Teaching involves not only the transmission of knowledge but also complex human interactions, empathy, mentorship, and understanding individual student needs. AI and robots can assist with certain aspects of education, such as providing personalized learning materials and assessments, but they may struggle to fully replace the nuanced and empathetic role of a human teacher.
2. **Individualized Learning**: AI can help tailor educational content to individual students' needs, providing personalized recommendations and feedback. However, truly effective teaching often involves adapting to students' emotional and social cues, which can be challenging for machines.
3. **Moral and Ethical Education**: Teaching extends beyond academic subjects; it includes imparting moral values, ethics, and social skills. AI lacks the capacity for moral reasoning and ethical guidance that human teachers can provide.
4. **Creativity and Critical Thinking**: Teaching often encourages creativity, critical thinking, and problem-solving skills. While AI can assist in these areas, it may struggle to foster creativity and independent thought to the same extent as human educators.
5. **Social and Emotional Learning**: Human teachers play a vital role in supporting students' social and emotional development. They provide guidance, mentorship, and a supportive environment that fosters emotional intelligence and interpersonal skills.
6. **Adaptability**: AI-driven robots can excel at delivering pre-programmed content, but they may struggle to adapt to unexpected situations, individual student needs, or rapidly changing curriculum requirements.
7. **Acceptance and Trust**: The acceptance and trust of students and parents are crucial in education. Replacing human teachers with robots may face resistance from stakeholders who value the human touch in education.
While AI and robotics can complement the work of teachers by providing tools for personalized learning, automating administrative tasks, and assisting with certain educational processes, it's unlikely that they will fully replace human teachers and professors, especially in settings that value the holistic development of students. Instead, the future of education may involve a combination of human educators and AI-driven technologies working together to enhance the learning experience and make education more effective and accessible.
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Ciansong Pan
Shenzhen University
Under current technical conditions, I don't think so. AI driven Humanoid robots have extensive knowlege, but can't teach every student from their personalities and abilities. Therefore, the quality of teaching is not expected.
Subharun Pal
Swiss School of Management
The notion that AI-driven humanoid robots could potentially supplant human educators in academic settings is a subject of considerable epistemological and ethical inquiry, warranting nuanced, interdisciplinary exploration. While it is incontrovertible that advances in artificial intelligence, particularly in machine learning algorithms and natural language processing capabilities, have engendered unprecedented efficacies in the administration and delivery of pedagogical content, there exist dimensions of educational praxis that remain inherently resistant to automation.
At the core of this resistance is the principle that education transcends mere transmission of informational content or knowledge commodification. Instead, pedagogy often functions within the framework of Paulo Freire’s dialogical model, where pedagogical transactions serve as a conduit for the cultivation of critical consciousness, meta-cognitive skills, and humanistic values. This involves complex social interactions and relationships that are deeply embedded in tacit, experiential knowledge—what Michael Polanyi would describe as "the personal coefficient"—which encompasses aspects of empathy, motivational scaffolding, and context-sensitive interpretive skills.
The efficacy of the human educator is in part predicated upon their ability to engage in what Jerome Bruner terms “scaffolding” — a nuanced, dynamic process wherein the educator provides contextual, emotionally sensitive guidance, thereby enabling the learner to achieve higher cognitive function. It is this capacity for affective attunement, moral and ethical mentoring, and the dialectics of Socratic discourse that artificial intelligences, irrespective of their algorithmic sophistication, are currently ill-equipped to replicate.
Additionally, the praxis of teaching is not merely a technical endeavor but exists within a sociopolitical context. Human educators often act as agents of social justice, engaging in critical pedagogy that challenges systemic inequities. This requires an interpretive understanding of societal structures and cultural nuance, and a capacity for advocacy and ethical decision-making that lies beyond the purview of current AI technologies.
That said, AI can certainly serve as a potent pedagogical adjunct. Its capacity for data analytics, adaptive learning pathways, and automated administrative functions can liberate educators from the quotidian burdens of logistical minutiae, thereby enabling them to focus on higher-order educational objectives. This suggests a symbiotic paradigm wherein AI serves to augment rather than replace human agency.
In summary, while the incursion of AI-driven humanoid robots into the educational milieu offers tantalizing prospects for enhanced efficiency and personalization, the holistic complexities of educational praxis render it unlikely that these artificial entities will obviate the need for human educators in the foreseeable future. Therefore, rather than harboring existential anxieties about occupational obsolescence, educators might productively engage with AI as a tool to amplify their pedagogical efficacy, thereby realizing a more emancipatory and transformative educational paradigm.
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To what extent, AI would be able to do justice towards incorporating and replicating the actual reservoir physics?
Discussion
2 replies
  • Suresh Kumar GovindarajanSuresh Kumar Govindarajan
Artificial Intelligence in Reservoir Engineering
1. How exactly a reservoir engineer would respond to an AI system with reference to a real oil/gas field scenario as on date?
2. Why should a reservoir engineer have any ‘Algorithm Aversion’ (the so called negative attitude towards using Algorithms), if AI could understand the reservoir heterogeneities precisely?
3. Whether the task of reservoir planning and management could be considered as ‘subjective’?
OR
Would it require attention to individual uniqueness (such as an expert in reservoir draining principles; or, an expert in reservoir characterization; or, an expert in reservoir risk and uncertainty quantification)?
4. How exactly an individual reservoir engineer’s perceptions of a ‘Reservoir Characterization and Drainage Principles’ (RCDP) could influence her/his attitudes and behavioral intentions, which, in turn affect their actual use of AI technology?
Whether a reservoir engineer would perceive the actual usefulness of AI technology towards RCDP;
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Would she/he perceive the ease of using AI technology?
OR
The way AI technology is presented, it gets framed, it gets designed, and the way it gets marketed – is going to influence the perceptions of a reservoir engineer towards AI’s usefulness and its associated ease of use?
5. Even while applying simple reservoir engineering principles, for example, how exactly a reservoir engineer would be able to deduce the average value of a reservoir permeability - depends on - striking a perfect balance between her/his sound theoretical knowledge as well as to the extent of data availability.
On top of it, if we focus the problem, purely based only on data (as expected by AI), even then, the way porosity data needs to be handled; and, the way, the permeability data needs to be handled have fundamental differences - arising from the fact that ‘porosity data pertain to Gaussian distribution’; while ‘permeability data pertain to log-normal distribution’.
On the other hand, mere data on porosity and permeability may not always help us to deduce the actual least resistive pathways, which the reservoir, by default would prefer (where, the dynamic capillary forces are overcome with ease over various pore-throat sizes)?
If so, to what extent, AI would be able to do justice towards incorporating and replicating the actual reservoir physics?
6. Leaving aside the engineering principles, to what extent, emotional and psychological responses associated with an individual reservoir engineer towards RCDP could play a crucial role towards reservoir planning and management?
Feasible for an AI to have a coupled cognitive and emotional responses to RCDP?

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