Risks and Barriers to ‘edtech’: Overcoming Roadblocks in Incorporating Data-Driven Technologies and AI in Education

In my previous blog post, I discussed the advantages of incorporating AI and data-driven technologies in the education sector, drawing from the report “AI Barometer Part 5 — Education” published by the Centre for Data Ethics and Innovation (CDEI) and the Department for Digital, Culture, Media, and Sport (DCMS). Today, I want to delve into the risks and barriers that come with incorporating data-driven technologies and Artificial Intelligence (AI) in education, as highlighted in the same report.

The AI Barometer is the CDEI’s effort to understand the most pressing challenges and barriers associated with AI and data use that prevent us from reaping the benefits they offer, particularly in education, to build back better.

Risks

The AI Barometer identified the following risks associated with the use of AI and data-driven technologies:

(i) Algorithmic bias — where data collection and processing is primarily of a personal and sensitive nature and where decisions supported by data-driven technology have significant impacts on individuals’ choices and opportunities

(ii) Risk of automating poor pedagogical practices — there is a broad range of approaches to teaching, some of which are easier to automate using data-driven technology, but panellists, such as academics and experts, were concerned that mismatches between market offerings and educator needs

(iii) Privacy and autonomy concerns regarding data collection and retention in education environments — panellists raised a set of privacy and autonomy concerns over private and sensitive data on learners, as well as on education professionals that are typically very easy to de-anonymize given their specificity.

(iv) Data usage by third-party vendors — education professionals, learners, and their guardians do not have a clear understanding of how data is used, and the provision of data subject consent is not always meaningfully obtained.

(v) Long-term data stewardship concerns — panellists highlight concerns around long-term data stewardship and data retention or deletion.

Barriers

Barriers to responsible innovation are the issues that prevent us from maximizing the benefits of AI and data-driven technologies in education. Some of these barriers include:

(i) Limited market understanding of educator needs — educators may not fully understand the capabilities of data-driven technologies and how they align with their specific needs.

(ii) Poorly accessible market information — educators may not have access to all the information they need to make informed decisions about data-driven technologies.

(iii) Low trust in EdTech — educators may not trust the efficacy of data-driven technologies, leading to a lack of adoption.

(iv) Inadequate and inaccessible data — data-driven technologies rely on accurate and relevant data, but educators may not have access to the data they need to make use of these technologies.

Solutions — Next Steps

The CDEI has proposed to help overcome barriers to innovation by establishing an AI assurance ecosystem and addressing the information and communication challenges of AI in education. Through desk research, pilot projects, and expert engagement, the CDEI has identified six priority areas for developing an effective, mature AI assurance ecosystem in the next five years:

(i) Generating demand for reliable and effective assurance across the AI supply chain

(ii) Building a dynamic, competitive AI assurance market that provides a range of practical services and tools

(iii) Developing standards that provide a common language and scalable assessment techniques for AI assurance

(iv) Building an accountable AI assurance profession

(v) Setting out regulatory requirements that can be assured against

(vi) Improving links between industry and independent researchers so that researchers can help develop assurance techniques and identify AI risks.

In conclusion, AI and data-driven technologies have the potential to significantly enhance learning and teaching. However, there are risks and barriers that need to be addressed to fully realize these benefits. The government is taking steps to ensure that AI and data-driven technologies are responsible, trustworthy, effective, and compliant. It remains to be seen how successful they will be in this endeavor.