The research team, led by Preston R. Osborn of the University of Minnesota Duluth along with colleagues at The Ohio State University, framed the work against a persistent problem. Racial inequality remains entrenched across American systems, including healthcare, criminal justice, housing, education, and child welfare, and helping professions have historically struggled to address disparities in service quality and mental health outcomes within racialized communities. Social workers occupy a unique position to respond, guided by professional ethics that emphasize social justice and competence. Yet the field’s educational standards, including the Council on Social Work Education’s requirement that programs prepare students to understand the mechanisms of oppression and demonstrate anti-racist practice at multiple levels, have outpaced the pedagogical tools available to meet them.

That gap is partly a measurement problem. Anti-oppressive practice, a framework that examines oppressive power structures and equips practitioners to collaborate effectively with clients from diverse backgrounds, rests on three core tenets: reflexive practice, intercultural communication skill, and demonstrated anti-racism. But assessments of these constructs in social work education typically depend on self-reporting and instructor subjectivity, both of which are vulnerable to social desirability bias. Prior research has shown that individuals consistently overestimate their own cultural competence; in one cited study, social work participants who significantly endorsed colorblind attitudes linked to racial prejudice nonetheless rated themselves as culturally competent. Programs also tend to infuse diversity content thinly across required coursework rather than concentrating it, an approach associated with weaker culturally responsive outcomes than dedicated diversity curricula.

The ISAOP Model was designed to translate anti-oppressive concepts into practical professional application. It integrates three domains drawn from cultural humility and cultural consciousness literature: critical reflexivity, meaning an understanding of one’s own intersectional positionality; cultural humility, meaning openness and adaptability to cultural differences; and anti-racism, meaning social justice advocacy at multiple levels. Uniquely, the model reorients ecological systems thinking by shifting focus to the individual’s capacity to influence the systems around them, moving from personal adaptation toward social-ecosystem-level change. It explicitly centers race, with the stated goal of dismantling forms of racism and white supremacy within social work practice settings, and uses a dialectical process to probe students’ understanding of their own thoughts, behaviors, and attitudes during intercultural engagement.

The theoretical engine behind the intervention is transformative learning theory, which describes how individuals shift their worldview when confronted with experiences that challenge existing beliefs. In this framework, a disorienting dilemma prompts critical examination of one’s assumptions, leading to self-examination, recognition of discontents, and the construction of a self-developed plan of action. The researchers argue that this individualization matters because students arrive at diversity education with profoundly different emotional needs. BIPOC students may need space to feel heard and affirmed while processing the challenges of dismantling oppressive systems, without being compelled to educate their White peers. White students, meanwhile, may grapple with tension between the desire to distance themselves from oppressive systems and their self-image as good people. A private coaching space, the team reasoned, could serve both without centering anyone’s experience at the expense of others in a group.

The coaching protocol itself followed seven steps. After completing pretest surveys, each participant met virtually with a coach, one of a team of doctorate-level social work students who consulted with an advisor experienced in solution-focused therapy. The session opened by introducing professional areas where the participant might apply anti-oppressive skills, reviewed the ISAOP model, and then examined the participant’s personalized assessment report, which assigned rudimentary high, moderate, or low ratings across reflexivity, intercultural effectiveness, and anti-racism. Using motivational interviewing techniques and a strengths-based approach, the coach and student explored the student’s interpretation of their scores, co-developed a plan of action, and closed with space for questions. Sessions lasted between 48 and 71 minutes, averaging 56 minutes, and were recorded, transcribed, and supplemented with field notes to support fidelity and validity.

Measurement relied on three validated instruments. The Self-Reflection and Insight Scale captured cognitive, affective, and behavioral processes of self-reflection, with subscales showing internal consistency coefficients of 0.91 and 0.87. The Intercultural Effectiveness Scale measured factors of effective intercultural communication across five subscales, including behavioral flexibility, interaction relaxation, and message skills. The Anti-Racist Behavioral Inventory assessed knowledge, awareness, and behaviors related to anti-racism, spanning individual advocacy, awareness of racism, and institutional advocacy. Data were collected between January and April 2021, with pretests in January, coaching sessions in February and March, and posttests and feedback surveys in April, all administered virtually through Qualtrics and Zoom.

The qualitative analysis yielded five themes that map the students’ inner journeys. Participants connected past experiences and identities to current practice, with one describing ongoing healing and curiosity about her own culture as a foundation for engaging others. They evaluated their cognitive and behavioral processes, with several candidly acknowledging biases they were working to unlearn and one noting that politeness without genuine relationship-building could read as cold or confusing to clients from different cultures. They named barriers to skill development, most prominently fear, including the fear of being perceived as racist, which made one participant hesitant to adapt her communication style, and logistical strain, with one student asking how to juggle work, school, and personal life while also being an advocate. They planned concrete behavior changes, from bringing their authentic selves to interactions to expanding their social circles beyond predominantly White networks to solicit honest feedback. Finally, they reported strong satisfaction with the private, one-on-one format, with one participant calling the experience thought-provoking and another praising the personalized feedback as insightful.

The quantitative picture was more muted. Pretest reflexivity scores were already high, averaging 81.7 percent of the standardized range, and rose 2.5 percent by posttest. Intercultural effectiveness scores stayed in the moderate range, gaining 1.1 percent. Anti-racism scores showed the largest movement, climbing 5.7 percent from the moderate range into the high range. Wilcoxon signed-rank tests, chosen because the data violated normality assumptions, found none of these changes statistically significant, a result the authors attribute largely to the sample of nine students. They also raise a subtler interpretation: students may have overestimated their skills at baseline, so coaching may have produced more accurate self-assessment rather than inflated scores. A single session may also have been insufficient, since effective coaching interventions typically involve multiple sessions over longer periods, and students were contending with heavy academic demands during pandemic-era restrictions.

The study’s implications reach beyond social work. The authors argue that personalized learning within diversity education can help educators tailor activities to individual experiences, needs, and professional goals, and that separate or individualized spaces can protect racialized students from having the White experience centered in group discussions, which research suggests can subtly redirect attention and energy away from people of color. Using graduate students as coaches, rather than course instructors who hold evaluative power over participants, may also foster the rapport needed for honest self-reflection. The team plans to carry these lessons into a larger federally funded workforce development program, the Behavioral Health Workforce Education and Training Program, which prepares professionals to deliver culturally responsive services to BIPOC and LGBTQ+ youth and families. Planned refinements include increased session dosage, longer intervention duration, focus groups for feedback, and automated generation of individual coaching reports. Acknowledged limitations, including the small, non-representative sample, convenience sampling, absence of a control group, and the possible inhibiting effect of session recordings, temper the findings. Still, the pilot makes a case that teaching future social workers to confront racism may work best not in a lecture hall, but in a private conversation built around their own scores, stories, and fears.

Subject of Research: Individualized anti-oppressive practice coaching as an educational intervention for social work students

Article Title: Individualized anti-oppressive practice coaching (I-AOPC): a pilot study on anti-oppressive practice in social work education

Article References: Osborn, P. R., Hughley, A., Stepney, L., & Lee, M. Y. (2025). Individualized anti-oppressive practice coaching (I-AOPC): a pilot study on anti-oppressive practice in social work education. Journal of New Approaches in Educational Research, 14(1), Article 17. https://doi.org/10.1007/s44322-025-00036-x

Image Credits: AI Generated

DOI: 10.1007/s44322-025-00036-x

Keywords: social work education, anti-oppressive practice, anti-racism, cultural competence, transformative learning theory, coaching intervention, reflexivity, intercultural communication, diversity curriculum, pilot study, personalized learning, graduate education

The core of the framework is a tri-lens analytical matrix that crosses three dimensions. The first dimension consists of three moral traditions: utilitarian ethics, which weighs aggregate outcomes and costs; deontological ethics, which focuses on duties, rights, and rules that must not be violated regardless of consequences; and virtue ethics, which asks what a good practitioner with good character would do in a given situation. The second dimension distinguishes AI’s two operational arenas. Frontstage systems are those clients directly encounter—chatbots, screening tools that shape decisions about their families, monitoring devices in their homes. Backstage systems are the algorithmic administration that clients rarely see: automated eligibility checks, data-matching pipelines, and risk-scoring infrastructures that quietly sort populations. The third dimension consists of four constitutive social-work values: privacy, self-determination, social justice, and human dignity. Every AI deployment in social welfare, the paper contends, can be located within this twenty-four-cell matrix, and each cell generates distinct ethical questions that generic AI-principle frameworks tend to blur.

This specificity matters because social work is not a generic domain for AI ethics. The discipline is anchored in relational practice—the idea that change happens through sustained, trusting relationships between practitioners and the people they serve—alongside care ethics and anti-oppressive praxis. A framework borrowed wholesale from medicine or finance risks missing what is distinctive about welfare encounters: they involve people in situations of dependency, often coerced or involuntary, where the state itself is both the service provider and the surveillance authority. Hwang argues that existing scholarship documents many harms of welfare AI—bias, opacity, surveillance, and the erosion of professional discretion—but remains conceptually fragmented, producing case-by-case critiques without a shared diagnostic language. The matrix is an attempt to supply that language, and to make explicit that different AI modalities pose different risks requiring different safeguards.

To demonstrate the framework’s reach, the paper applies it to three purposively selected cases whose heterogeneity spans continents and technologies. The first is the Allegheny Family Screening Tool in the United States, a predictive risk model used in child welfare screening in Allegheny County, Pennsylvania. The second cluster concerns algorithmic welfare administration: the Dutch SyRI system, which mined government data to detect suspected welfare fraud until a court curtailed it; the Australian Robodebt scheme, which automated debt recovery against benefit recipients and became one of the country’s worst public-administration scandals; and South Korean crisis-household detection, which uses administrative data to identify families at risk. The third case examines digital-care technologies in Korea and OECD comparator countries, where sensors, wearables, and remote-monitoring platforms mediate care for older adults. Together these cases show that a child-welfare risk score, a fraud-detection pipeline, and a care robot are not ethically interchangeable, even though all are labeled AI.

The frontstage–backstage distinction does much of the analytical work here. A frontstage tool like the Allegheny screening model directly shapes what a caseworker sees and does at the moment of first contact with a family. Its errors are consequential and intimate: a falsely high risk score can trigger intrusive investigation, while a falsely low one can leave a child unprotected. Because the tool operates at the point of professional judgment, the framework treats it as ethically permissible only if it augments that judgment—informing the worker’s assessment without displacing relational authority. A backstage system like SyRI or Robodebt, by contrast, operates on populations at scale, often without individual knowledge or meaningful consent. Its harms are structural: wrongful debt notices, discriminatory targeting, and the conversion of welfare applicants into suspects. The framework suggests that backstage automation faces a higher ethical bar precisely because no relational check exists to catch its errors before they land on people’s lives.

The three moral traditions also pull in different directions, and the matrix makes those tensions visible rather than hiding them. A utilitarian assessment of a predictive screening tool might emphasize aggregate benefits: earlier identification of at-risk children, more efficient allocation of scarce caseworker time, and prevention of tragedies. A deontological assessment asks whether the system respects the rights of the people it scores—whether they know they are being scored, whether they can contest the score, and whether consent is meaningful at all. A virtue-ethical assessment asks what the technology does to practitioners: does it cultivate attentiveness, humility, and care, or does it train workers to defer to a number and abandon the interpretive habits that relational practice requires? The paper’s position is that an AI system must survive scrutiny from all three lenses, not merely the consequentialist one, because welfare systems have historically justified harms to individuals by appeals to aggregate efficiency.

The four social-work values function as the normative anchors of the analysis. Privacy is threatened not only by data collection but by inference: a risk model can reveal intimate patterns of family life that no one deliberately disclosed. Self-determination is eroded when algorithmic outputs narrow the options a client is offered or the discretion a worker exercises. Social justice, the paper stresses, must be substantive rather than merely formal—a system can satisfy formal nondiscrimination while still distributing burdens unevenly across already-marginalized groups, because the underlying data reflect historical inequities. Human dignity is at stake whenever a person is reduced to a risk score or a fraud probability, processed by machinery they cannot see or question. The framework asks of every deployment: does this system advance these four values, or does it quietly trade them away for administrative convenience?

From the case analyses, the paper distills a set of conditions under which AI in social work is ethically defensible. First, AI must augment practitioner judgment without displacing relational authority—the human relationship remains the locus of decision, and the algorithm serves as one input among many. Second, AI must advance substantive rather than merely formal equity, which requires auditing outcomes across groups and correcting for the inequities embedded in historical data. Third, AI must be embedded in contestable, auditable institutions: affected people must be able to challenge algorithmic decisions, independent bodies must be able to inspect the systems, and the terms of deployment must remain politically revisable rather than locked in by technical opacity. Where these conditions fail, the same technology crosses from augmentation into automation, and the paper’s analysis suggests that resistance is then not Luddism but an ethical obligation grounded in the profession’s core commitments.

The Robodebt and SyRI episodes illustrate what failure looks like. Robodebt automated income-matching and debt issuance at scale, shifting the burden of proof onto vulnerable recipients and generating unlawful debts before a royal commission exposed the scheme’s flaws. SyRI’s opaque data-mining targeted low-income neighborhoods and was ruled in violation of the European Convention on Human Rights by a Dutch court in 2020. Both systems were backstage, uncontestable in practice, and structurally biased toward suspicion of the poor—precisely the configuration the framework flags as automated welfare. By contrast, the paper’s treatment of digital-care technologies suggests a more promising frontier, provided that monitoring remains genuinely consensual, that data flows serve the person being cared for rather than only the institution, and that human contact is not replaced by sensor readouts. The Korean crisis-household detection case sits between these poles, showing how administrative data can surface families in need—but only if detection triggers supportive outreach rather than punitive intervention.

The paper’s contribution is threefold, and its implications reach beyond social work. It reframes the field’s debate from replacement versus resistance to augmentation versus automation, a shift that turns a stale binary into a designable and governable distinction. It supplies a reusable diagnostic matrix that practitioners, agencies, and regulators can apply to concrete systems before deployment rather than after harm. And it integrates Korean, European, and North American evidence within a single analytic lens, demonstrating that the dilemmas of the digital welfare state are global even as their legal and institutional responses remain local. As governments worldwide race to embed AI in public services, the framework’s central warning is worth stating plainly: the question is never whether AI can make welfare administration faster or cheaper, but whether it leaves the human relationship—the actual substance of social work—intact. The research was supported by the Asan Foundation and a Gachon University research fund, and was presented at the Asan Foundation Symposium in Seoul in May 2026.

Subject of Research: An ethical framework for evaluating artificial intelligence as augmentation or automation in social work

Article Title: An ethical framework for assessing artificial intelligence as augmentation or automation in social work

Article References: Hwang, K. (2026). An ethical framework for assessing artificial intelligence as augmentation or automation in social work. Discover Social Science and Health. https://doi.org/10.1007/s44155-026-00463-x

Image Credits: AI Generated

DOI: 10.1007/s44155-026-00463-x

Keywords: artificial intelligence, social work ethics, augmented intelligence, predictive risk modelling, algorithmic decision-making, digital welfare state, relational practice, care ethics, anti-oppressive practice, Allegheny Family Screening Tool, Robodebt, SyRI