Introduction to Generative AI and Educational Publishing

Having spent over a decade in the publishing industry, I’ve seen how the transition to digital communication and systems has brought incremental changes to the age-old processes of developing educational books. My recent experiments with ChatGPT and other AI tools have led me to believe that these technologies could be a catalyst of change for publishers. I’ve tried my best to craft the perfect prompt (a written instruction to ChatGPT) to generate lesson plans, summaries, multiple-choice questions, blurbs, marketing copy, and educational content. In doing so, I’ve seen that these AI models have the potential to significantly enhance product development pipelines, thus enabling even smaller, digitally-adept teams to create textbooks and other educational materials that rival the offerings of industry leaders.

Understanding Generative AI

But first, what exactly is generative AI? These systems use large language models, trained on vast amounts of data, to generate text by predicting the likelihood of the next word given a specific context. They are sophisticated prediction machines. Responses are generated using prompts. These detailed instructions combine context, examples, data, and restrictions. With advanced language capabilities, these large language models (examples include ChatGPT, GPT-4, Claude and Bing) can impact how educational content, such as textbooks, are written and revised by assisting authors, editors, and proofreaders in automating tasks, structuring content and refining text. By automating various tasks and generating highly-customisable material, publishers can streamline their workflows and explore new business opportunities. It’s exciting times ahead for publishers willing to disrupt themselves!

The rapid rise of generative AI in the workplace also means new skills are required. Organisations must prioritise a continuous learning culture and focus on skilling employees in digital literacy, AI and data analytics. Publishers need to upskill staff to understand how to use data-driven approaches to educational content development while adapting workflows to take advantage of the efficiencies offered by generative AI. I’ve set up a personal learning pathway for myself in these areas, which I will be cascading down to my team. 

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Why Data Literacy should be taught in African Schools

In a world increasingly shaped by data and statistics, the capability to comprehend and harness the potential of data is valuable. Just as literacy is the ability to read and write, data literacy includes reading, working with, analysing, and arguing with data. From healthcare and business to environmental conservation and education, data is the linchpin of the decision-making fabric. Our daily lives are steeped in data – weather forecasts, financial transactions, health records, and even social media engagements. In the same way, media literacy equips individuals to navigate the torrent of information, data literacy is the compass by which we chart the course through the sea of data around us. This article will explore the essence, components, and significance of data literacy, especially for high school learners, who are the future torchbearers of innovation and progress.

What is data literacy?

Data literacy is the skill to process, interpret, critically evaluate, and communicate data. To be data literate means not just reading numbers or graphs, but understanding the sources of data, the methods of collection, and how it can be used for analysis and decision-making. Essential skills include understanding basic statistics, data cleaning, visualisation, and making data-driven arguments. It is a fusion of quantitative skills with critical thinking. Being data literate is akin to being fluent in the language of data; it enables you to have meaningful conversations and make informed decisions based on data.

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Diji: Empowering African Youth Through Language-Specific Digital Learning

Diji is an innovative digital learning platform providing short, skills-based courses in African languages. The platform’s primary objective is to empower post-school, unemployed, or under-employed African youth by offering learning opportunities in their native languages. Delivered via mobile browser or chat applications, Diji makes learning accessible and practical.

Diji’s development process was guided by the Successive Approximation Model (SAM), an agile instructional design approach that involves repeated cycles of development and refinement. SAM’s iterative process allowed for a more tailored and efficient development of a learning platform aimed at empowering African youth through accessible content in their native languages.

The Challenge

During SAM’s ‘Preparation Phase’, a needs analysis was conducted to understand the target audience and identify the gap in accessible, skills-based learning resources in African languages. This phase established the groundwork for the platform’s objectives and informed the subsequent design and development stages.

The Solution

The ‘Iterative Design Phase’ of SAM played a critical role in the development of Diji. The feedback from over 8,500 people was vital in shaping the platform through several iterations. The content was designed to be data-conscious and tailored for accessibility on mobile browsers and chat applications. This iterative approach allowed Diji to refine its platform to meet the user’s needs effectively. Additionally, the partnership with, who provide access to their messaging platform, exemplified how collaboration during this phase can enhance product development. The platform was piloted with Zulu and expanded to include Xhosa, Swahili, and Yoruba.

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