Monitoring and Evaluation in the Artificial Intelligence (AI) Fanfare - Part 1

Let me just rant a bit before I get educational....

Just put it in Chat-GPT!

Co-pilot writes my emails!

Gemini just summarises when I Google it.

AI this!

AI that!

SIGHHHHHHHHHHHHHH…………. Quite frankly, I am just about over this craze about Artificial Intelligence (AI)! Whether it's the AI upgrades to the apps on my phone, AI integration in existing and new systems at work, scrolling through my feed and seeing the latest news on AI developments, or an ad telling me how to make millions using AI and how AI is about to take over the world. Now I love progress as much as the next Gen-Z, but this is literally how I feel with all these AI talks :

If you haven’t heard the hype about Artificial Intelligence (AI), I wonder where you've been, and I'm kind of envious that I wasn’t there with you too! While ignorance is bliss, I guess being in the "know" has its “pros” too.

Rant over for now...Time for being educational...

Quick Information Technology (IT) Lesson :-

Artificial Intelligence is not new; however, the COVID-19 pandemic in 2020 created conditions that made the digital world thrive. Pushing forward 2 years later, in 2022, there was the significant launch of OpenAI's ChatGPT. There has been a digital frenzy ever since. Although 2022 wasn't the beginning of AI innovation, the launch certainly served as a major catalyst for the field. The Generative Pre-Trained Transformer (GPT) was made public and required no technical training for use due to its natural language processing capabilities. Non-technical users can interface directly with and instruct the bot in human language using a prompt, receiving results instantly. Sounds fancy, eeh? But as for me, I couldn't even be excited or interested cause I barely knew the significance of a technology like this, but I can't be ignorant forever, right? So let's begin with the basics - what is Artificial Intelligence (AI) ?

Artificial Intelligence (AI) refers to technology (computational systems) that can simulate tasks typically performed by humans, such as problem-solving, creativity, comprehension, reasoning, language learning, and decision-making.  There are several forms of Artificial Intelligence that can be categorized based on the capability of the technology to simulate these human tasks. Though there are several forms of AI, the real AI hype these days has been about “GENERATIVE AI”.

Generative AI (Gen-AI) refers to technologies that utilise natural language processing and machine learning models to generate new content, including images, text, audio, code and video. The focus of this technology is rapid content creation based on prompts.

Examples of Gen-AI are ChatGPT, Gemini, Canva AI and Microsoft Co-pilot.

So now that we have that IT class lesson out of the way, we can get into the really important stuff - is it really all that?

Honestly, I have my apprehensions about this AI thing. To add fuel to the fire, they claim that AI is about to take our jobs. There's no sense in being scared at this point, so that leaves me with my curiosity. I work in the field of monitoring and evaluation and I’m quite curious if AI can do this job, and if yes, how and what parts can it do? If no, why can't it do it?

My quest for answers began by doing some digging to see how AI is being applied to M&E, if at all. Here's what I found:

1. Writing and Reporting

   Writing proposals and reports is a constant for M&E. For me, it’s a no-brainer; if there is anything I would love AI to help me with, it's to magically turn a page from blank to an entirely written report. AI has been applied to writing in several ways, whether it is a bot that can check vocabulary, grammar, spelling, punctuation, style and tone, or bots that can give M&E templates for proposals and plans when prompted. The assistance from AI does not stop at reports and proposals, but also assists with the writing of communications by drafting emails, letters, meeting notes, and the like.

   
   

2. Data Collection has been the top area I've seen AI integrated. This is moving beyond basic computer data entry. AI is being used to provide immediate transcription for discussions, focus groups, and interviews, utilising voice recognition technology. AI has been implemented to help with automated data collection from different sources and provides real-time data validation (spell-check, data type check, etc.) of data that is being collected in the field with the use of electronic tools such as tablets before it is sent to a centralised system.

   
   

3. Data Analysis has been a welcome area for AI innovation. Analysis can be hard; however, AI has aided in data analysis through its rapid analysis features. It can summarise data in real-time; it also suggests ways in which data can be analysed (formulas, etc.) and visualised (charts, etc.). AI has been particularly utilised in qualitative analysis to aid in identifying themes and patterns in text documents. Another example is the use of computer vision to analyse videos and images for the identification and classification of key aspects. AI has also been used to perform predictive analysis to determine project outcomes and check trends based on historical data.

   
   

After finding out these three areas of application, I am simultaneously intrigued and a little distressed by how much there is to learn, but we press on! My research revealed that my apprehensions may have been fueled by ignorance and a lack of curiosity about how M&E and AI could be intertwined. I grudgingly admit that significant strides individuals and groups have made to utilise AI for M&E work. This utilisation has given way to the introduction of AI into existing systems, as well as the development of new systems. One thing that surprised me the most was that there were existing AI-centred applications created specifically for M&E purposes. Wow! I love a tailored tool.

For now, I don’t believe it will be a replacer, unless we get to “Super Intelligence” level, right? However, for us M&E folks, adaptability is not new to the nature of our field. As such, I hope our approach (even with apprehensions) to AI is one that seeks to leverage opportunities by being explorative, intentional, problem-solving oriented and ethical when using it, seeing AI as an enabler and tool to the M&E process. While my digging has me warmed up, I now realize I have some new thoughts. Check back for Part 2, where I will share my experience on giving my hand a try at integrating Gen-AI into my work. See you then!