Artificial Intelligence, Artificial Teachers and the Fate

International Journal of Pure and Applied Mathematics Volume 119 No. 16 2018, 2245-2259 ISSN: 1314-3395 (on-line version) url: http://www.acadpubl.eu/hub/ Special Issue

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Artificial Intelligence, Artificial Teachers and the Fate of Learners in the 21st Century Education Sector: Implications for Theory and Practice Ikedinachi Ayodele Power Wogu1, Sanjay Misra2, Esther Fadeke Olu-Owolabi3 Patrick A. Assibong4, Oluwakemei D. Udoh5 1, 3, 4, 5

2.

. Department of Political Science & International Relations, College of Leadership Development Studies, Covenant University, Ota, Ogun State, Nigeria. Department of Electrical and Information Engineering, College of Engineering, Covenant University, Ota Ogun State, Nigeria.

. [email protected] [email protected] [email protected] [email protected] [email protected]

Abstract. Recent studies on High Level Machine Intelligence (HLMI) systems, especially in the areas of Artificial Intelligence (AI), Robotics Engineering and Nanotechnology, tends to further strengthen the resolve that machines will by 2020, taken over at least 20 million jobs in every woks of life. Another study from Oxford University, found substantial evidence to argue that AI’s in the next decade, would revolutionize the education sector, outperforming teachers in areas like language translation, analytical thinking and in critical essay writing for high school levels. This new revolution for most scholars further questions the relevance and the fate of teachers and learners in the face of rising advances in AI technology. While the Marxian Alienation Theory is adopted as theoretical framework for the study, the Ex-post factor and Derrida’s analytic methods of the social science was adopted for the study. Artificial Teaching Assistants (ATA), capable of doing better than human teachers now abound in leading institutions of learning, but with ontological and psychological implication for the leaner. Government is employed to train tomorrows’ work force with the capacity to work hand in hand with machines rather than competing with them. Keywords: Artificial Intelligence, Artificial Intelligence technology, Artificial Teachers, Education Sector, Extinction risk fears, HLMI, Marxian Alienation’s Theory, Robotics Engineering.

1. Introduction On September 11th, 2017, Sir Anthony Sheldon predicted that: “Artificial Intelligence (AI) technology will replace the best teachers of the future with intelligent machines” [1]. The consequences of this prediction, he noted, would totally transform the way education is carried out in the next 10 years. Hence, he believed that the place of teachers in class rooms will become that of setting up equipment and maintaining

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discipline while machines take over the real job of teaching. In other words, human teachers will become the assistants while the real job of teaching and education will be left to Artificial Intelligent Machines. Several studies in this regard reveal that Sheldon is not alone in this opinion: [1][2][3][4]. To corroborate the claim of an era of total machine take over, a Georgia Tech Professor and Scientist: Ashok Goel, explained how he utilized all the advanced AI systems at his disposal to create a single HLMI system known as ‘Jill Watson’, a sophisticated system that runs on IBM Watson-Powered processors [10]. This system was discovered to favorably pass all the tests and inquisitions thrown at it by graduate students and all who came to her for assistance. Consequently, may believe the success with Jill Watson, is one the anticipated success and evolution expected to hit the educating sector [3]. These technological advancements notwithstanding, there are those who believe this era would spill more doom for the education industry than the benefits which scholars now debate about. Thus, questions like: Will the era of machine takeover in the education sector be possible in theory or in practice? What implications would the era of machine takeover have on mankind? The problematic of the study: This paper is largely inspired by the rising consequences of the ‘extinction risk fears’ stirred up amongst workers in the education sector, a fear orchestrated by various alarming predictions from AI researchers, about when HLMI systems would fully take over the jobs and profession of teachers in the education sector. In more specific terms, the paper is inspired by the following problems: i. Recent predictions foretell that the jobs of teachers as it is known today, will soon be replaced by HLMI systems. ii. The original essence of education is presently at the risk of extinction. iii. The era of machine takeover will have adverse ontological and psychological consequences on the minds of learners. iv. There exist now, ‘an extinction risk fear’ that has shaken the foundation, essence and the values of educations in the 21st century. Objective of the paper: In the light of the above problems, this paper strives to: i. interrogate and analyze the various predictions directed at identifying when HLMI systems would take over the jobs and functions of teachers in the educations sector. ii. critically evaluate the claims that HLMI systems is a panacea to 21 st century education challenges. iii. analyze and critically evaluates the ontological and psychological implications for adopting HLMI systems for educational purposes in the 21 st century. iv. via the analysis of relevant data, evaluate the effect of the extinction risk fears among workers and to identify its implications for theory and practice for adopting HLMI systems for 21st century education. Methodology: The Marxian Alienation Theory which espouses the kind of estrangement persons experience as a result of unpleasant situations and experiences they are

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daily confronted with while seeking to making a living from the society they live in [11][12][13], is adopted for this study, because it offers researchers viable platforms for assessing and evaluating the objectives outlined for this study. The Ex-post factor methods for conducting research in the social science [14][15] and Derrida’s reconstructive and deconstructive analytical method for interrogating the meaning of concepts and all the arguments [36][37][38], offered by scholars on the subject matter of this study, is adopted. It is hoped that this methods and theoretical foundation would increase the validity and quality of deductions envisages to be made for the aims and objectives proposed for this study.

2. A Review of Predictions on when HLMI Systems will Take Over the Education Sector Recent studies [1][3] indicate that Anthony Seldon is not the first to suggest probable dates when HLMI systems will acquired all the capacity required to successfully take over man’s job as a teacher in the class room. A similar modeling study from ‘Oxford Martin School, predicted that 47% of the jobs in the world are currently at the risk of becoming automated’ [16] as a results of AI advances in the 21st century. These authors are unanimous in the opinion that the overall effect of these advances will cumulate in ‘a radical disruption of human lives’ [32]. The next few pages shall- via tables, charts and Gamma CDF Diagrams – present an analysis of the results of a study conducted by Katja Grace [17] which largely focused on identifying the timeline median estimates when HLMI systems/intelligent machines would fully automate human jobs and activities. Some of the results gotten from the study revealed that most participants expected that AI would outperform humans in tasks such as: language translation by [2024]; critical speech and essay writing for high school students by [2026] and truck driving jobs by [2027] [2]. Table 1, Fig. 1 and Fig. 2 offers graphical illustrations to this effect. Table 1: Prediction and distribution by region of most jobs that would be auto mated in the nearest future

Source: Adopted from (Grace, Salvatier, Dafoe, Zhang, and Evans, 2017) Arxiv.org

The respondents however felt that for human jobs like working in retailing outfits, writing bestselling texts and Novels or working as a medical Surgeons in Hospitals, would take as long as [2031, 2049 and 2053] respectively, before machines gain total control and automation of these jobs [2]. The experts in this study believed that the entire human jobs in the world – in 120 years’ time from now - will be fully automated. However, they warned that there is a 50 % chance that AI could outperform man in all tasks in another 45 years from now, by reason of the prevalence of what has

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been described as the massive adoption of ‘High-Level Machine Intelligent HLMI [2] systems in every facet of life. Table 1: clearly shows the predictions and various timelines when each item on the table is expected to be overrun by intelligent machines. The items that concern this article are those which foretell when intelligent machines would emerge and become capable of taking over the jobs of academics and teachers in institutions of learning. The data gathered from respondents in Table 1 indicates that the predictions made about the time when AI machines would emerge in all the regions of the world, on the average, is largely on the high side (80.0 +123.6+109.0+14.6). Results from the entire study put together, indicates that, while AI experts from North America were inclined to believe that AI machines will outperform humans within the space of 74 years from now, AI researchers from the Asian region, were of the opinion that machine autonomy would take place in just less than 30 years from now [17]. The authors of this paper can’t help wondering what piece of knowledge the Asians have about AI research that is quite different from the knowledge which the Americans have on AI research, vice versa. (see Table 1). Other predictions worthy of note are those made regarding the time frame before machines acquire the full capability to translate languages by [2024] and the time it would take for HLMI systems to have full capacity to write high school essays, by [2026] or to become capable of writing bestselling Novels by [2049] [5]. (see Fig. 2).

Source: Adopted from (Grace,et al, 2017) Arxiv.org. Source: Adopted from (Grace,et al, 2017) Arxiv.org

Figure 1: The aggregate predictions for HLMI Figure 2: The aggregate predictions by year

2.1

The Role of HLMI Systems in the 21st Century Education Sector

It is generally believed that one of the pertinent functions of AI machines in the education sector is to compliment the role of teachers in class rooms when it is able to take over those time consuming and seemingly lower level tasks like grading and keeping records of the scripts graded. However, the existence and proliferation of very sophisticated HLMI systems for today’s educational purposes, tends now to

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question what roles AI should play in today’s schools. This section discusses a few of the roles which AI plays in the education sector. The emergence of automatic grading systems: In the past, grading test scripts and examinations papers were laborious tasks. It was and still is, a task most teachers find difficult to grapple with because, the process of grading takes up significant portions of their time to process. Hence, the emergence of easy grading software that makes use of Machine Learning Algorithms (MLA) [18] is considered a plus for the education sector. Even though this HLMI software is still in its infancy stage, its adoption for examination and test purposes takes off a huge burden from the shoulders of teachers, thus allowing them ample time to focus on class activities and students interaction [18]. The emergence of software for identifying and modifying courses: While it is a fact that teachers may not always know where the gaps and limitations are in their course/lecture/education materials, which often results to getting students more confused than they can afford to in a particular course, HLMI systems are known to provide ways of solving such problems when they are deployed. Coursera, a large MOOC online educational platform is known to have already implemented this systems as a way of enhancing their service to students. The system for instance, is able to quickly detect when a large number of students are submitting the wrong answers to a project. The system is thus able to notify the tutor and give students hints that aids to improve the quality of choices they make during such projects/examinations [19]. 2.2 The Teacher, the Art of Teaching/Tutoring and the Artificial Teacher Perhaps, the best point to start discussion on the above sub titles, is by offering conceptual clarification for the terms used in this section: the teacher/tutor/teaching and the artificial teacher. The authors of this paper found the illustrations/definitions by Dan Schultz and Jenifer Prescott of ‘we are teacher’s.com [34], very handy for this passage. It reads: We are teachers, we are leaders, and we are young and old men and women. We are readers and thinkers, dreamers and doers. We are magicians. We make learning happen whatever the resources. We are scientists and inventors. We are Artists and musicians. We are sometimes the only attentive adult in a child’s life. We teach poetry and politics. Architecture, History and that the stages of mitosis are prophase, metaphase, anaphase, telophase and cytokinesis and we do it all while managing dozes of unique, brilliant and challenging personalities. We teach children to read, to write, to solve, to question, to be compassionate, to be global citizens. We teach kids that it’s ok to make mistakes and even to fail. We know that some of the most teachable moments arise from failure. Every day we greet an audience and step onto a stage. Sometimes we deserve an academy award. Other times we deserve a do-over. We are the leaders, the motivators, but never the stars, our students are. We light the fire of imagination and wonder, and we keep those flames alive when kids are losing hope. We give children the keys to devise, construct and live their own future. We have perfected the art of controlled chaos and reading upside down. Of eating our

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lunches in five minutes flat, of holding it in so we can make it until the bell rings to go to the bathroom and still we love each and every moment and each and every student as if they were our own. But get it straight, we are not babysitters. We are not in it for the summer off and we are not one in the same, we come from all works of life and teach in all kinds of schools and we each have our own style. We do, act, make, solve, create, nurture, love, compel, build, grow, support, transform. We are teachers we change the world… [34]. This conception of who tutors/teachers are, speak volumes about the goals, aspirations and the objectives of the tutor/teaching profession. They are goals clearly defined and engrained in the very foundations that defines what education stands for [18]. The authors of this paper therefore wonder why some scholars argue that the above mentioned goals can be attained via the platform of education acquired/transmitted via Artificial Teachers? The study by Survey Monkey and others similar to it, indicates that artificial creations are not known to have the capacity of delivering the original goals and objectives of education, nor can they partake of the essence which the institution of educations originally belongs to [35]. (See Fig. 3, Fig. 6 and Fig. 7).

3. A Critical Analysis of the Viability of Human Teachers and Artificial Teachers in the Light of HLMI Systems The authors as this point - via the analysis of vivid data and clear cut argumentsstrives to provide an analysis of the viability of Artificial Teachers/Artificial Teaching Assistants (ATA) over Human Teachers in the education sector.

Source: Adopted from Survey Monkey (Watkins, 2017). [35]. Source: Adopted from Survey Monkey (Watkins, 2017) [35].

Fig. 3: The Viability of Robots as Teachers. Fig. 4: On the Extinction Risk Fears 3.1 A critical analysis of the roles of HLMI systems in the education sector Results from recent studies [35][19][35] conducted to observe and analyze the impact and the implications for the rapid adoption of HLMI systems in today’s schools and institutions of learning, revealed that while some advantages were identified to be associated with rising implementation of HLMI systems for educational purposes, there are yet some hazards which, when considered on the average, seem to make of

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no effect, the advantages identified to be connected with the rising adoption of HLMI systems used for learners as indicated in Fig. 3, Fig. 6 and Fig. 7. In this regard, responses gathered depicts that 92% of respondents were willing to attest that despite the seeming benefits of HLMI systems for educational purposes, the pertinent role of teachers cannot be replaced by robots. This section discusses a few of such examples. Massive adoption of HLMI systems distorts the valuable teacher-student relationship: One of the profound impact of the growing adoption of HLMI systems in the education sector, is that it increases and promotes the learner’s inability to study or learn independently of online platforms or via other mediums such as the Artificial Teacher Assistance (ATA). When situation like this takes place, it exposes learners to certain psychological, social and emotional challenges which makes it difficult for them to mix with other students in a natural society. Studies reveal that most learners who use these online mediums/HLMI systems, tend to miss out in their ability to develop critical 21st century skills and behaviours that are essential for critical thinking and problem solving as indicated in Fig. 6, where 75% of respondents argued that there were more benefits to learning in a traditional on-campus learning environment with human teachers, over the kind of learning that take place on online platforms

[35]. Source: Adopted from Survey Monkey (Watkins, 2017). [35]. Source: Adopted from Survey Monkey (Watkins, 2017). [35]

Fig. 6: Online vs On-Campus learning. Fig. 7: Benefits of On-campus learning Figure. 7 illustrates how advantageous it is for students who pass through school with the right kind of exposure from interacting with human teachers in on-campus settings HLMI systems are known to bread natural inequality among learners: Since HLMI systems naturally customize learning to suit every individual in a group or a class, there is the tendency for such systems to want to tailor the course work for each student according to the capacity of the learner involved. By so doing, HLMI systems without knowing it, meddles with the standardization that is expected of the course or program. Consequently, while some students progress rapidly, a disturbing proportion of learners wallow in the state of retrogression, thus breathing inequality among the weak and strong students [19]. The results from the survey conducted in Fig. 7 is an example of the resultant effect of a wide range of inequality amongst students that

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emerges in the place of learning, which is responsible for the different kind of skills individuals in schools end up with upon graduation. These skills at the end of the day is what defines and differentiates one individual from another. The survey reveals that 23% of the responses gathered affirmed that their exposure to direct skills from specific learning fields during their on-campus training is what gave them the edge and the chances of succeeding and doing better in life and in their society upon graduation. 3.2 The Theory and Practice of Artificial Teachers/HLMI Systems in the 21st Century Education Sector Education has been perceived as the ability to pass on knowledge from one person to another through acceptable mediums [21][8]. The ability to acquire and disseminate knowledge, studies [8] reveal, is one of the oldest practices which man is able to engage in that sets him apart from every other living being on earth. It is via this platform of education that one generation is able to advance and consolidate on the gains of the previous generations before them. No doubt, the turn of the 21st century has witnessed tremendous transformations in the way the teaching and the learning processes are conducted. The process indeed has gone through an unprecedented transformation, courtesy of the innovations in the Information and Communication Technology (ICT) industry. Hence, what was conceived to be impossible some 20 or 40 years ago has now become possible both in principle/theory and in practice. The advent of AI technology in the 21st century has further redefined the way learning and educational processes are conducted in class rooms and in institutions of learning all over the world. It is thus the opinion of this paper that, while the student-teacher relationship in an educational setting, is one that could be described as ‘so complex and human in nature’ [18], the same cannot be said about the kind of relationship that is presumed to exist between the Artificial Teaching Assistants (ATA) and numerous online student relaying on it for one form of assistance or the other. Thus, a future where human teachers are replaced by Artificial Teachers (HLMI systems, Robots, ATA, Chartbots, running on AI algorithms and various Operating Systems OS) - in the opinion of this paper – is very inimical to the future of learners [18]. However, while the data presented in Table 1, Fig. 1 and Fig. 2 may make the whole idea of machines take-over practicable in theory, such scenarios cannot yield the anticipated results and objectives of education in practice. The data and argument presented in Fig. 3 and Fig. 6, corroborates this point. This point needs to be emphasized, despite the fact that one cannot entirely rule out the scenario where advances in HLMI systems could advance to the point where they actually taking over the jobs of mankind as predicted by [17].

4 Ontological and Psychological Implications for Adopting HLMI Systems in The Education Sector: The Marxian Perspective Ontological implications for adopting HLMI systems in the education sector: Studies [22][4][23] on AI research, which focused on raising awareness about the

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ontological implications and consequences of the massive adoption of HLMI systems for schools, institutions of higher learning and the education sector generally, were observed to be more responsible for generating what the authors of this paper chose to refer to as ‘the scary extinction risk fears’[24]. This risks places man in a disadvantaged position where he loses his beingness and his essence for living. This is because the advent of HLMI systems and the speed at which jobs are being automated, places mankind at a great risk of losing his job and mind to machines who had over the years, acquired intentionality and intuition [7][9]. These features makes it possible for machines to acquire human like characteristics, which makes it possible for them to be capable of competing favorably and more efficiently with man over his place as the being at the helm of affairs. More importantly, the feature of intentionality makes it possible for HLMI systems to successfully automate routing and none routing jobs in the education industry. The successful invention of the Artificial Teaching Assistants (ATA), otherwise known as the ‘Artificial Teacher’, by a Georgia Tech Professor, adds credence to the fears which is now being demonstrated by many in the education sector. Other individuals with sophisticated jobs from all works of life have expressed one form of fear or the other as a result of these recent advances in HLMI systems. Recent studies [5][6][6][20], some of which have already been discussed in the above pages, clearly predicts the time when these total take-over are expected to take place as indicated in Fig. 2 and Fig. 4 The chart in Fig. 4 for instance, signifies that up to 60% of the respondents who to part in the study by survey monkey where willing to attest that the soon coming era of machine take-over is a threat to their beingness and existence. Hence, the thought of this machine take-over has heightened the tensions and psychological/ontological trauma which scholars in the academia and the ordinary man on the street now grapple with. Kelvin Drum expresses this extinction risk fear in this manner: I want to tell you straight off what this story is about: Sometime in the next 40 years, Robots are going to take your job. I don’t care what your job is. If you dig ditches, a robot will dig them better. If you’re a magazine writer, a robot will write your articles better. If you’re a Doctor, IBM’s Watson will no longer “assist” you in finding the right diagnosis from its database of millions of case studies and journal articles. It will just be a better Doctor than you. Until we figure out how to fairly distribute the fruits of robot labor, it will be an era of mass joblessness and mass poverty [20]. Thus, from the Marxian Alienation point of view [25], the kind of fear expressed in the above context by [20] is the kind that manifests in all the classes of the Marxian Alienation Theory: (1) Alienation of the worker from his work and its product. (2) Alienation of the worker from working and production. (3) Alienation of the worker from what Karl Marx called “their Gattungswesen (species-essence) and (4) Alienation from human nature [25][26][8]. The authors of this paper however, believes that the 3rd and the 4th class of Marxian Alienation are the classes most expressed in the ontological lamentation expressed in the above quotation by [20]. It is one of the adverse ontological/psychological implications of the rising adoption of HLMI systems

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among academics, scholars and the ordinary man on the streets whose job is on the line today. Psychological implications for adopting HLMI Systems in the Education sector: This part of the study evaluates the implications of the proliferation of HLMI systems in the education sector. The psychological implication arising from adopting HLMI systems, most scholars like Sabastian Thrun in [27] and others like [28][29][30] believe, is one of the most serious factor affecting the learner, the tutor and even the vendors or operators of online education platforms. The public lamination by Sabastian Thrun, the Founder of Udacity who mourned over their failure to use HLMI systems in their MOOC online platforms to deliver on the expected goals of education [27], is a typical example worthy of note. Other kinds of psychological implications include: Technology addiction and the Distancing effect. While other factors exist, only this two factors were discussed in this paper. Technology Addiction: This is one of the foremost challenges associated with the massive adoption of IA technology as the only viable mode for acquiring and transmitting any form of knowledge amongst 21st century learners. Where this becomes the case, the operators of these education platforms, run the risks of causing a majority of learners on their platform to become liable to what today is referred to as ‘technological addicted’. Learners affected by this kind of addiction tend to rely totally on systems, devices or technological gadgets etc., as the only viable medium of doing virtually all life’s activity. Consequently, such learners are found wanting and limited, without the capacity to perform anytime they are faced with situations which does not allow them to use the platforms or modes of learning which they are already accustomed to. The derogatory feeling caused by technological addition is expressed in the Marxian Alienation theory. Indeed, this kind of technological addiction manifests in either of the four classes of the Marxian Alienation Theory already discussed above in [12][25][26][13]. Learners affected by this factor find it difficult to replicate very essential models of behaviour from their esteemed teachers/tutors [35]. The presence of this factor is inimical to youths who already have psychological problems. The ‘Distancing effect’ factor is another implication worthy of note. It is a challenge that affects students who are separated in time and place from their object and subject of study [31][32][33]. Other related implications in this category include: the morally distancing, psychological distancing and psychological propinquity factors [33]. 4.1 Summary of findings Put quite simply, the study conducted for the 1st objectives revealed that, there is a 50% chance that machines will have the capacity to acquire full autonomy over the jobs of human in the space of 120 years from now [17][6]. While it seems that most of the predictions and time lines presented in [17] are alarming, more alarming is the fact that most of these HLMI systems will be able to acquire autonomy in certain fields of endavour, much earlier than the anticipated time predicted. The data in Fig. 4 among other studies considered for this paper highlights the extinction risks fears expressed by workers and scholars. Data in Fig. 1 and Fig. 2 adds credence to the

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claims that there might not be any task familiar to humans which will be impossible to machines soon. On the subject of HLMI systems being the solution to the problems of 21st century education, evidence gathered from studies considered for this paper, gave the authors sufficient reasons to reject the claims that HLMI systems is a panacea to 21 st century education challenges. A critical evaluations of the studies considered for the 3rd objective of this paper, revealed that increasing reliance on HLMI systems by education learning platforms and education vendors, have appropriated the roles of humans to HLMI systems, leading to adverse implications like (1) a lack of personal connection, (2) technological addiction, (3) the propensity to cheat on the platform, (4) the rising high failure and dropout rates for majority of students who use these HLMI systems. These implications in one way or the other, manifests all or either classes of the Marxian Alienation theory. Studies considered for the 4th objective, revealed that most of the theories and predictions advocated about the rising advances in AI research, HLMI systems and the extinction risks threats are largely underrated, since most of the predations made about when machines would attain certain level of advancements, were proven not to be entirely correct. Most of the predications made about the era of machine take-over seem to have come to pass much earlier than the initial time earlier anticipated. The exponential increase in machine learning research is attributed to this factor. Hence, this paper is inclined to believe that the era of machine take-over is a lot closer than it’s feared—so close, in fact, that it may be starting already’[20]. 4.2 Recommendations In the light of the above findings, the authors of this paper are inclined to make the following recommendations:  Institutions of learning should strive not to give more preference to Artificial Teachers over Human Teachers/tutors because of the adverse implications that have been proven to be associated with attempting to replace Human Teachers with Artificial Teachers.  Relevant government agencies, institutions of learning and employers of labour are enjoined to equip learners of this dispensation with the skills and capacities they will need to work hand in hand and alongside intelligent machines, as against the present efforts directed at competing with HLMI systems in today’s workplace.  Before now, the most talked about subject matter in the globe is the need to consolidate on the efforts made for the wide spread deployment of renewable energy. The fact is that it has already gotten enough attention. Renewed efforts needs to be made towards giving the impending AI storm all the attention and publicity it needs so as to properly prepare for the time when this possible Machine take-over might take place.

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5 Conclusion From the studies considered for the objectives of this paper, the authors provided substantial data and rational grounds to submit that the rising proliferations of Artificial Teachers via HLMI systems is far hazardous than beneficial to the fate of learners all over the world. The presence of the ontological and psychological implications identified to affect mankind and learners adds credence to this submission. Future researchers should among other things, focus on finding ways of developing human intelligence to reduce rising complexities resulting to the feeling of alienation among the workforce. References 1.

Houser, K..: The Solution to Our Education Crisis Might be AI. Futurism, an Online Publication of Future Society (2017). https://futurism.com/ai-teachers-education-crisis/ 2. Edmond, C.: This is when a robot is going to take your job, according to Oxford University. Formative Content, World Economic Forum. An online publication of the World Economic Forum. (2017). https://www.weforum.org/agenda/2017/07/how-long-before-a-robot-takes-yourjob-here-s-when-ai-experts-think-it-will-happen 3. Talwar, R., Wells, S., Calle, H.: 10 human jobs disrupted by AI. IT ProPotal Online. (2018). https://www.itproportal.com/features/10-human-jobs-disrupted-by-ai/ 4. Hawking, S., Tegmark, T., Russell, S and Wilczek, F.: Transcending complacency on super-intelligent machines. Hoffpost. Online publication; (2014). h t tp :// www.h u ffin gtonp ost .c om/ st ep h en - h awk in g/ arti fi ci al -in t elli g en ce_ b _5 1 742 6 5 .h t ml 5. Ratner, P.: Here's when machines will take your job, as predicted by AI gurus. Big Think Edge. Online publication. (2017). http://bigthink.com/paul-ratner/heres-when-machineswill-take-your-job-predict-ai-gurus. 6. Gray, R.: How long will it take before your job is automated? BBC Capital News Online. (2017). Retrieved on the 26th February, 2017. http://www.bbc.com/capital/story/20170619how-long-will-it-take-for-your-job-to-be-automated. 7. Schrager, A., Wang, A.: Imagine how great universities could be without all those human teachers…Quartz Online. (2017). https://qz.com/1065818/ai-university. 8. Wogu, I. A. P., Olu-Owolabi, F. E., Assibong, P. A., Apeh, H. A., Agoha, B. C., Sholarin, M. A., Elegbeleye, A and Igbokwe, D.: Artificial intelligence, alienation and ontological problems of other minds: A critical investigation into the future of man and machines. Conference paper presented at the 2017 ICCNI/IEEE International Conference on Computing, Networking and Informatics. In proceedings of the IEEE International Conference on Computing, Networking and Informatics (ICCNI 2017). 29-31 October, (2017), Covenant University, Ota. DOI:10.1109/ICCNI.2017.8123792. 9. Wallace, H.: The Future of Artificial Intelligence in the Poker World. CASINO GAMS PRO. Online Publication.(2017). http://www.casinogamespro.com/2017/10/16/the-futureof-artificial-intelligence-in-the-poker-world 10. BOSS Magazine.: Artificial Intelligence in Education. Innovation in Artificial Intelligence. An online publication of Boss Magazine. (2016). https://thebossmagazine.com/aionline-education/ 11. Mészáros, I.: Marx's Theory of Alienation. Online publication. (1970). http://www.marxist.org/archive/meszaros/works/alien/. 12. Ollman, B.: Alienation: Marx's conception of man in capitalist society. Online publication. (1976). http:www.alienationtheory.com/.

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13 13. Cox. J.: An introduction to Marx’s theory of alienation. (79) 5 International Socialism: Quarterly Journal of the Socialist Workers Party (Britain) Published July 1998 Copyright © International Socialism. (1998). 14. Cohen, L, Manion, L & Morison, K.: Research methods in education. London: Routledge Falmer. (2000). 15. Marilyn, K.: Ex-post facto research: Dissertation and scholarly research, Recipes for success. Seattle, WA: Dissertation Success LLC. (2013). http://www.dissertationrecipes.com/wp-content/uploads/2011/04/Ex-Post-Factoresearch.pdf. 16. Paterson, C.: Artificial intelligence in education: Where it is at, where its headed. Getting Smart. An online publication. (2017). http://www.gettingsmart.com/2017/10/artificialintelligence-in-education/ 17. Grace, K., Salvatier, J., Dafoe, A., Zhang, B and Evans, O.: When will AI exceed human performance? Evidence from AI experts. arXiv:1705.08807v2 [cs.AI] 30 May 2017. arXiv:1705.08807v2 [cs.AI]. 18. Ghafourifar, A.: 14 ways AI will impact the education sector. Entefy online publication (2017). https://venturebeat.com/2017/07/23/14-ways-ai-will-impact-the-education-sector/ 19. Tech Thought.: 10 Roles for AI in education. Tech thought. An online publication of techthought (2014); https://www.teachthought.com/the-future-of-learning/10-roles-forartificial-intelligence-in-education/ 20. Drum, K.: You will lose your job to a robot – Sooner than you think! Mother Jones. Online Blogger. (2017). Retrieved on the 26th February. https://www.motherjones.com/politics/2017/10/you-will-lose-your-job-to-a-robot-andsooner-than-you-think/ 21. Wogu, I. A. P.: Problems in mind: A new approach to age long problems and questions in philosophy and the cognitive science of human development, Pumack Nigeria Limited Education Publishers. pp. 495; ISBN 978-978-50060-7-0. (2011). 22. Bryant, M.: Artificial intelligence could kill us all. Meet the man who takes that risk seriously. The Next Web (TNW) Online publication. (2014). https://thenextweb.com/insider/2014/03/08/ai-couldkill-all-meet-mantakes-risk- seriously/#.tnw_hVchaHqU 23. Russell, S.: This artificial intelligence pioneer has a few concerns. Quanta Magazine, Online publication (2015); https://www.wired.com/2015/05/artificial-intelligencepioneer-concerns/. 24. Davey, T.: Artificial Intelligence and the Future of Work: An interview with Moshe Vardi. Future of Life. Online publication. (2017). https://futureoflife.org/2017/06/14/artificial25. Gouldner, A. W.: The two Marxism. New York: Oxford University Press, pp. 177–198. (1984). 26. Dictionary of Philosophy (DOP).: ‘Alienation. In The Dictionary of Philosophy: Revised 2nd (Eds), p. 10. (1984). 27. Holton, D.: “The spectrum of opinion about MOOCs”. Centre for Teaching and Learning Excellence, (CTLE) Embry-Riddle Aeronautical University, (2013) 28. Vardi, M. Y.: Will MOOCs destroy academia? Communications of the ACM, 55(11). (2013). Retrieved from http://cacm.acm.org/magazines/2012/11/156587-will-moocsdestroy-academia/fulltext. 29. Basu, K.: "Faculty groups consider how to respond to MOOCs". Inside Higher Ed. (2013). Retrieved 2013-10-13. https://ipfs.io/ipfs/QmXoypizjW3WknFiJnKLwHCnL72vedxjQkDDP1mXWo6uco/wiki/ Massive_open_online_course.html. 30. Rivard, R.: "EdX Rejected". Inside higher education. (2013). Retrieved 22 April 2013. Available at: http//www.insidehighered.com/news/2013/04/19/despite-courtship-amherstdecides-shy-away-star-mooc-provider.

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14 31. Wogu, I. A. P., Atayero, A. A. A., Olu-Owolabu, F. E., Sholarin, M. A., Ogbuehi,U. K., Akoleowo, O and Ubogu, P. C.: The changing face of education and the dilemma of Massive Open Online Courses (MOOCS) in Nigeria’s tertiary institutions: Implications for development. A paper delivered at the 3rd International Conference on African Development Issues (CU-ICADI2016). (2016). https://scholar.google.com/citations?user=J5h7gSwAAAAJ&hl=en 32. Rubin, R.: “Moral distancing and the use of information technologies: The seven temptations. In J.M. Kizza, (Ed). Social and ethical effects of the computer revolution, (pp. 124125). Jefferson: McFarland and Company, (1996). 33. Russell, G.: “The distancing dilemma in distance education. On line publication of faculty of education, Monash University (2004). http://www.itdl.org/journal/feb_04/article03.htm, 34. Schultz, D and Prescott, J.: We are teacher.com (2017). 35. Watkins, J.: Why you should care: Because the future ain’t slowing down for nobody. The daily dosage. August 2017. Ozy Poll publications online. (2017). https://www.ozy.com/acumen/ozy-poll-full-results/80468. 36. Derrida, J.: Force of law: Deconstruction. Translated by Mary Quaintance, eds. (1992). 37. Derrida, J.: Of grammatology; Baltimore: Johns Hopkins University. Press; (1976). 38. Balkin, J. M.: Deconstructive practice and legal theory, 96 Yale L.J. (1987).

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