Improving the Education Systems and Innovation for A Better Future in Artificial Intelligence
As modern technology is rapidly evolving, the nature of educational development and the entire education system becomes increasingly important since their development fosters further technological enhancements. Technological innovation theoretically and practically broadens access to education in the current information age and encourages sustainable development. Many within the educational field ascribe to the transformative function of technology, and hence the ability and process of acquiring and using knowledge are fundamental. The progress that will be attained by a prospective technology such as Artificial Intelligence (AI) relies on the extensive improvements made in current education and innovation systems. Therefore, this discussion intends into how education and innovation could build the future of AI in a country. This will also include which improvements could be made to these sectors for a better future in AI.
The Development of Education and Innovation Systems for a Better Future in AI
AI is drastically fundamental to the world’s future since it established the foundation of computer learning. Through AI, computers can harness extensive data and utilize their learned intelligence in making optimal decisions and discoveries within a fraction of the time human beings would take (Thomas, 2019). AI is increasingly becoming responsible for the medical breakthroughs that are currently prevailing in cancer research to the cutting-edge climate change research being done. Therefore, it is evident that the technology’s potential is unmatched, and any measure that will see its development needs to be implemented fully (Thomas, 2019). Education and innovation systems provide one of the best foundations that will allow the development of AI to reach greater levels in the future.
It is prudent to also note that attaining a better future for the AI sector will require countries such as Saudi Arabia to position themselves as a global hub where the best respective products are made a reality. Countries need to establish a solid presence on the global landscape of AI via its publicly available contributions (Saudi Arabia National Strategy for Data & AI, 2020). This will guide the country to have a comprehensive plan for the country’s contributions in both the local and global initiatives and events. In actual fact, countries should ensure that they create distinctive platforms where universally thinking leaders and main stakeholders in the AI sector are able to exhibit, debate and shape the future trajectory of AI within the government space, private sector and non-governmental organizations. These engagements will amplify the technology’s positive value for humanity (Saudi Arabia National Strategy for Data & AI, 2020).
The Development of A Data-Driven Education System
Education stakeholders have acknowledged that the system requires better tools to diagnose it entirely, identify any learning gaps, and determine the appropriate strategies to handle them (Custer et al., 2018). First, developing a data-driven education system should start by producing a quality curriculum that incorporates learning about the current digital systems (Twining et al., 2021). The present digital era has become very influential in human life. Notably, technology continues to advance at a fast pace, and individuals of all ages must be equipped with both technical knowledge and skills required to navigate the present digital society.
In relation to Artificial intelligence, a limited number of higher education courses teach on this technological concept, leading to a few professionals in the field. This is unfortunate considering that AI is going beyond just being designed and programmed in a way that they think and act like a human. The innovation is currently being used in a wide area of daily services (GIS User, 2019). However, while every other application or productivity tool acknowledges AI as an essential resource for development, there are minimal explanations in what AI is, its technological concepts, and how human beings could leverage it to improve their personal lives and company operations. Therefore, there is an educational gap whereby there needs to be a vast number of majors and even the introductory course that any school person can learn about and understand.
Specialists have indicated that they are constantly developing artificially intelligent programs for learners to work as human assistants. The AI programs will indeed work together with humans who will assist in finishing different undertakings such as reading something and putting it in human terms for human beings to relate easily. These developments will need a data-driven educational curriculum that ensures all learners have the skills and knowledge required to interact with AI programs. Consequently, a fully equipped academic curriculum in terms of AI knowledge and skills sets a better path for innovation, considering that many individuals who understand it will engage in further innovation and development.
Nevertheless, it is prudent to note that the education system currently lacks investments made in creating and managing data despite being the proper stages that will provide useful information. Therefore, national governments must invest in developing a functional and reliable information system that will collect data, store it, and have the capacity to share and disseminate it for informing both views and decisions (Custer et al., 2018). In numerous countries, education management and information systems (EMIS) are limited in their structures capacity and implementation; the data sources are typically fragmented across redundant information systems within a similar ministry or several of the ministries with different subsectors. Notably, these weaknesses have come from insufficient and unexpected funding. These systems will also be powered by governments and the development partners investing in building the capacity of the proper officials to collect better data and foster its utilization (Custer et al., 2018). Data collectors becoming its users will prompt improvements in the quality of information. Also, there is a need for collaboration across the spectrum that will ensure that the essential information required for decision-making is obtained and utilized accordingly.
With respect to a better future for AI, a data-driven education system will ensure that academic researchers from the junior to senior levels can exploit fully available information and make informed decisions in the respective projects they create. For any AI system that is being built, the availability and use of data will determine extensively how the system gets assessments, how the respective techniques and generally how it handles the intended challenge (Digital Curation center, 2020). The present innovation progress attained by AI has been extensively data-driven, which has led to a data economy. Therefore it is fundamental that academic researchers, among other users, have access to expensive amounts of data that can be analyzed through better education systems. According to Saudi Arabia’s National Strategy for Data & AI, the creation and access to open data has remained an important asset for any robust Data & AI ecosystem. Opening government data avails considerable opportunities to both the public and private organizations and individuals. This would lead to the creation of an attractive environment for startups; ensure a visibility in policy formation, decision-making and driving both economic growth and innovation.
Notably, even with the extensive emphasis on ensuring that every step taken in AI development is data-driven, it is fundamental that the data’s protection and privacy regulations are enforced across the board. A strong regulatory framework is required for providing high standards in relation to data protection and privacy in line with appropriate ethical standards. These regulations will ensure that the data obtained and analyzed is solely used for AI developments (Digital curation center, 2020). Data misuse should also be prevented to maintain public trust in AI systems and the decisions made from their projects. Furthermore, the development of streamlined information systems within the education field will ensure that the public also gets access to digital education, such as information on who owns the data and more specific information on AI being a science. Public involvement remains fundamental to building data corpora, such as aiding speech technologies in various languages (Digital curation center, 2020). Public data input will also help in using track and trace applications that allow authorities to build intelligence to spread diseases. Nonetheless, the impact of decisions made from quality data will start from establishing a data-driven education system where knowledge is obtained, created, and disseminated for essential purposes.
Establishment of True Partnerships Between the Public and Private Sector
A joint effort between the private sector and public governments is currently required for developing a more agile regulatory framework that responds to the advancing pace of disruptive technologies (Medeiros, 2020). Numerous private companies need to comprehend the AI tools and unexpected effects of regulation-making the points of view fundamental to public regulators. Public action is required to ensure compliance with AI police and enforce ethical requirements. This will be attained through coordinated efforts between distinct public actors. Different regulators with the same policy targets need to incorporate a global legislation language that encourages convergence (Medeiros, 2020). International standards with a universal language will aid in streamlining the adoption of private players that operate in several regions. Also, private actor cooperation is needed for widespread compliance with any new regulations.
Currently, the policies related to AI are typically value-based. They may fail to provide essential details on how the private actors can attain set objectives within specific use cases to comply with the policy (Medeiros, 2020). Therefore, a true partnership between the public and private actors will provide enough guidance to understand particular ASI tools from their suitable designs that will meet intended objectives, the development process, and its use. The regulations will include operational guidance and workable directives that have been established from the partnerships (Medeiros, 2020). Consequently, extensive innovation within the AI technologies will happen while following specific guidelines and with greater certainty on the effects of the developed AI applications.
A better future for AI will also include cooperation between academic institutions and the private sector. While these partnerships have faced numerous challenges, strengthening ties between the two sectors will inevitably result in technological change and a fundamental feature of the ‘knowledge’ society (Walt et al., 2002). The British Prime Minister asserted that the entrepreneurial universities would be as essential as the entrepreneurial businesses as they continually foster each other within the knowledge economy. Additionally, closer collaborations between the sectors will ensure that private resources are harnessed while public expenditures are minimized (Walt et al., 2002). The public sector will also be able to learn the superior management skills that the private sector has proven to have. Conversely, the private sector also has its overlapping interest by partnering with the public sector, such as capitalizing from the research outputs and expertise while promoting the socially responsible image.
The input of private resources in AI research and innovation will anchor the innovation ecosystem required for technological developments (Ho et al., 2021). The public sector, including the government, has played a fundamental role in subsidizing basic research allowing the academic research institutions to conduct high-risk research that will take extensive time to commercialize. Also, the present AI innovations disproportionately rely on the private sector. Notably rebalancing AI research towards long-term academic and non-commercial research that includes all stakeholders will ensure that pure, advanced research within the AI field is conducted and a better future for the technology is guaranteed (Ho et al., 2021). It is prudent that national governments take up the initiative to create an environment that can attract efficient and stable funds for qualified Data and AI investment opportunities. The future of the AI sector will require extensive investment amounts for it to grow. The investments could be encouraged by a fund that adds incentives by refuting the risk of subsidizing returns (Saudi Arabia National Strategy for Data & AI, 2020). To this effect, a financial mechanism being established together with an investors’ support program will surely attract both local and foreign investments that will foster further technological innovations in the AI sector. The partnership between different stakeholders will ensure that the short-term and long-term approaches incorporated by the research institutions are coordinated with a consideration of the existing constraints.
Implementation of Regulations That Are In Tandem with AI Innovation
Extensive studies have asserted that well-designed ‘hard law’ regulations will increase innovation primarily when implemented with incentives that hasten their adoption (Uppington, 2021). The European Commission implemented the first of its kind AIregulations with critics indicating that the laws will slow down AI innovation. However, if the EC follows a specific strategy, then the region is bound to become the hotbed of AI innovation that creates a better for it. First, the main drive for these regulations is the imposition of new requirements in high-risk Ai systems. The drafters of these regulations require the developers to deploy an AI quality management system that articulates the needs around high-quality data sets, record-keeping, oversight, accuracy, security, among other vital issues (Uppington, 2021). Consequently, the AI system providers who are not identified as high risk are needed to develop voluntary codes of conduct that will attain similar objectives. Ultimately the laws will limit the number of AI systems deemed to be high risk and define the high-level requirements without dictating how they are achieved. Therefore, a compliance system is created based on self-reporting rather than an arduous process.
Better regulations will spur AI innovations across the board which should also apply to both foreign and local developers. Other initiatives that need to be included include creating tax incentives for AI developers that heed the established regulations, reducing uncertainties around the established rules, and pushing for the adoption of AI quality management systems that the law requires of the AI developers.
Conclusion
The education and innovation structures play a fundamental role in the future that AI technologies will create. Notably, with the right strategies, this disruptive technology will develop applications that will revolutionize different sectors in terms of effectiveness and efficiency. Therefore, the right stakeholders need to implement the proper regulations, establish a data-driven education system, and build genuine partnerships between the private and public sectors.
References
Custer, S., King, E. M., Atinc, T. M., Read, L., & Sethi, T. (2018). Toward Data-Driven Education Systems: Insights into Using Information to Measure Results and Manage Change. Center for Universal Education at The Brookings Institution.
Digital Curation Center. (2020). The Role of Data in AI: Report for the Data Governance Working Group of the Global Partnership of AI. Trilateral Research School of Informatics, The University of Edinburgh.
GIS User. (2019). What is the importance of having an artificial intelligence course? Retrieved from https://gisuser.com/2019/02/what-is-the-importance-of-having-artificial-intelligence-course
Ho, D. E., King, J., Wald, R. C., & Wan, C. (2021). Building a National AI Research Resource.
Medeiros, M. (2020, November 16). Public and private dimensions of AI technology and security. Retrieved from https://www.cigionline.org/articles/public-and-private-dimensions-ai-technology-and-security/
Saudi Arabia National Strategy for Data & AI. (2020).Realizing Our Best Tomorrow Strategy Narrative.
Thomas, M. (2019, June 8). The future of AI: How artificial intelligence will change the world. Retrieved from https://builtin.com/artificial-intelligence/artificial-intelligence-future
Twining, P., Butler, D., Fisser, P., Leahy, M., Shelton, C., Forget-Dubois, N., & Lacasse, M. (2021). Developing a quality curriculum in a technological era. Educational Technology Research and Development, 69(4), 2285-2308.
Uppington, W. (2021, October 6). Driving AI innovation in tandem with regulation – TechCrunch. Retrieved from https://techcrunch.com/2021/10/06/driving-ai-innovation-in-tandem-with-regulation/
Walt, G., Brugha, R., & Haines, A. (2002). Working with the private sector: the need for institutional guidelines. Bmj, 325(7361), 432-435.Walt, G., Brugha, R., & Haines, A. (2002). Working with the private sector: the need for institutional guidelines. Bmj, 325(7361), 432-435.
