How might we build a teacher development toolkit through culturally sensitive research practice?

Taslima Ivy

Development research in South Asia has long been dominated by Western ways of knowledge production, leading to power imbalances and a disconnect with the very communities it aims to benefit (Khan, 2022). Western research methods often may not fully align with local knowledge systems, norms and preferences. This misalignment can potentially suppress participant voices (Mbah et al., 2023).

To address this limitation, culturally appropriate research practices have emerged, prioritising collaboration and respect for the values of the communities under study. These methods also aim to rebalance the power dynamics between researchers and communities. They do so by ensuring participant voices are heard throughout the research journey in a culturally meaningful way.

For example, some cultures rely heavily on storytelling to make sense of everyday experiences. In these contexts, using a storytelling approach to understand experiences might be more effective than traditional question-answer interviews. The reason being, it respects participants’ communication preferences, fostering an environment that encourages authentic expression of ideas (Chilisa, 2019).

In this blog, we discuss our ongoing journey of building a teacher development toolkit called Swavalamban* for and with teachers working in resource-constrained schools in West Bengal and Odisha, India. Our approach is rooted in a culturally sensitive framework that places teachers’ insights at the core of toolkit development.

Phase 1: ‘Adda’ 

Our first phase aimed to understand teachers’ contexts, aspirations, and challenges regarding professional development. To create a culturally meaningful space for teachers to share their stories, we adopted a Bengali conversational practice known as ‘Adda.’ 

In an Adda individuals not only tell and exchange, but also debate, challenge, question and create ideas together. It has a strong connotation of equal positioning and friendship among participants, effectively breaking down power imbalances or knowledge hierarchies. Furthermore, Adda’s emphasis on two-way interaction (i.e., debating, questioning back etc.) makes it an ideal approach for producing knowledge together.

We adapted the Adda practice for our research by asking teachers to bring in artefacts (images, links, lesson plans, any object they wished or an anecdote) representing 5 key research focus areas. The Adda was conducted around these 5 areas represented by the artefacts, ensuring a shared focus.

Phase 2: Data Analysis  

Currently, we are in the second phase, analysing data to understand teachers’ professional development experiences. Our goal is not only to determine what content should be included in the toolkit but also how and to what extent the toolkit’s design can address specific challenges and aspirations in context. 

Our analysis approach involves examining raw data, applying codes to label data, identifying outcomes i.e., what can we expect through participation in the toolkit, and pinpointing affordances or perceived action possibilities:

Fig 1: Data analysis approach

For instance, teachers highlighted the need for resources on ‘material adaptation’ for first-generation learners, indicating a content area that may be included in the toolkit. From a design perspective, this also suggested a toolkit affordance, which is a desire to ‘learn’ or ‘construct knowledge’ through toolkit use. More examples are provided below:

Raw Data	Code	Toolkit Outcome	Affordances
Exchanging ideas is the best way to learn about how to bring variety in the classrooms… We need group discussions and need to share views through blogs and vlogs…	Exchanging ideas	Community to exchange ideas	Social connection and community building
‘Updating needed, want ‘newness’, ‘feel stuck’, only ‘one-off’ training given, no sense of ‘continuity’ in context	Experience of lack of progress in professional development	Sense of progress and continuity	Process oriented learning and achievement (goals, badges, rewards etc.) Continued learning

Table 1: Simplified data analysis

         
We identified nine content areas (Adapting material, teaching first generation learners, interactive and joyful learning, language skills development, managing large classes, teaching heterogenous classes, using digital and non-digital technology, providing feedback, using multiple languages) and eight affordances (Knowledge construction, sharing resources, social connection and community building, co-creation, innovation, developing teacher language competence, process oriented learning and continuity, access to recent information).

These will serve as the foundation for our toolkit design. The third phase will involve the evaluation of the toolkit by teachers, hence incorporating teacher voices in all stages of the research.

*Swavalamban is a Hindi word meaning self-reliance

References:

Chilisa, B. (2019). Indigenous research methodologies. Sage publications.

Khan, R. (2022). Giving power back to the people. In ElsaMarie D’ Silva (Ed), Decolonising development in South Asia (pp, 3-7). The Aspen Institute.

Mbah, M. F., Bailey, M., & Shingruf, A. (2023). Considerations for relational research methods for use in Indigenous contexts: implications for sustainable development. International Journal of Social Research Methodology, 1-16.

AI for (whose) good? Implications for the ICT4D community

In recent years, AI for Good (AI4G) – or also AI for Social Good (AI4SG) – has become a catchy label for initiatives, gatherings and funding calls, all revolving around the potential of Artificial Intelligence (AI) and Machine Learning (ML) to achieve the Sustainable Development Goals (SDGs). This conversation, shaped by win-win narratives on the synergies between business and development/humanitarian stakeholders, has been mainly animated by technological corporations, global consultancies, and international organisations, and has coalesced around a plethora of publications and events bringing together corporate executives, start-uppers, policymakers and development practitioners. Big Tech like Google and Microsoft, just to name the most prominent ones, have translated this AI-driven commitment to the SDG agenda, for instance, into platforms to prevent and mitigate flooding, or databases to organize and make available petabytes of environmental monitoring data. 

However, these initiatives have so far yielded mixed results. Reviews of AI4SG projects show their uneven distribution across the SDG agenda, with the overwhelming majority of projects addressing SDG 3 (‘Good Health and Well-Being’). Other studies have suggested that the over-reliance on AI systems could contribute to the reproduction of structural inequalities and injustice built into the data sets used to train predictive and generative models. This risk is likely to be compounded by the cost-cutting logic underpinning the data collection and annotation processes that are central to ML and by the opacity of the systems, which hinders accountability.  

Moreover, the popularity of AI since early 2010’, when the boom of the neural networks, or deep learning, approach has rekindled the interest of the industry, has coincided with the increasing influence of the tech industry over AI research and ethics agenda through massive hiring of AI scientists, computing power, and large datasets. Big Tech in particular has positioned itself as the main force shaping the research trajectory and the policy and popular conversation around AI by leveraging not only technical and financial resources, but also its geopolitical clout. 

While the academic attention on the political economy and policy relevance of Big Tech is growing, the implications of Big Tech’s influence on the global development arena have hitherto been largely overlooked. Particularly relevant for this blog is the currently limited discussion on the continuity, discontinuity and tensions between ICT4D/DD and AI4SG. Is ICT4D surreptitiously being subsumed by AI4SG, or is it struggling to keep its own identity? 

Since ICT4D 1.0, and in the wake of the post-2015 agenda, which placed greater emphasis on the private sector as an agent of development, tech companies have become more prominent in the development space. With the rise of AI, corporate actors have taken the lead in going beyond siloed solutions to developmental challenges and focused instead on the expansion of data infrastructures in the Global South – the very infrastructures producing evidence on which policies are based. They have built their hegemony not only upon their technological supremacy, but also on crafting an AI-driven development discourse that has been largely legitimized by policymakers, donors, development practitioners, and, to some degree, academics. And yet, as suggested by critical humanitarianism scholars, AI4SG is grounded in a culture of “humanitarian neophilia”, in which an “optimistic faith in the possibilities of technology” is combined “with a commitment to the power of markets”. Through a “top-down approach that presupposes what good is”, AI4SG attempts to deflect criticism by spilling over into a moral sphere and transcending the domain of development and, eventually, politics. 

ICT4D can differentiate itself from AI4SG by continuing to put stress on the D of Development and keeping the spotlight on the political nature of the concept – a nature that the SG of Social Good is concealing behind a technocratic veneer.  

This is a conversation that ICT4D scholars and practitioners need to have sooner rather than later.